To solve the problem that the edge gray level of the soybean canopy near the ground is uneven, the gray level difference between the target and the background is small, and it is difficult to accurately and efficiently obtain the soybean canopy target area. This paper combined the multispectral imaging processing technology with the classical image segmentation method and proposed a soybean canopy extraction method based on the multispectral image processing technology. Five kinds of soybean multispectral images, including green light, near-infrared, red light, red edge, and visible light, were collected by Sequoia multispectral camera. A Gaussian smoothing filter preprocessed the original soybean multispectral images. The distribution characteristics of the gray histogram of the soybean canopy and background were analyzed. On this basis, the iterative method, Otsu method, and local threshold method were used to extract the canopy region in the original soybean multispectral image, and the image morphological open operation was used to refine and expand the background to avoid the influence of the interference noise in the image region on the recognition effect of soybean canopy. At the same time, the effective segmentation rate, over-segmentation rate, under-segmentation rate, information entropy, and running time were taken as the monitoring indexes, and the effect of the soybean canopy multispectral image recognition model was evaluated. The results showed that the iterative method could effectively segment the near-infrared and visible soybean canopy images, and the effective segmentation rate was 97.81% and 87.99% respectively. The segmentation effect of green, red and red edge soybean canopy images was poor, and the effective segmentation rate was less than 70%. Otsu and local threshold methods could effectively segment the other four kinds of multispectral soybean canopy images except for red light, and the effective segmentation rate was more than 82%. The effective segmentation rate of the three algorithms for red soybean canopy images was less than 20%, which did not achieve good results. In the original multispectral image, iterative method, Otsu method, and local threshold method were used to extract the mean value of information entropy of soybean canopy image and standard image, and the fluctuation amplitude was 0.120 1, 0.054 7, and 0.059 8, respectively. Otsu and local threshold methods were smaller, showing the effectiveness of the two algorithms in soybean canopy multispectral image recognition. The Otsu and local thresholding methods could effectively extract the soybean canopy images of green light, near-infrared, red edge, and visible light. Both of them retained the soybean canopy information completely. Otsu method had better real-time performance than the local thresholding method. The results provided a theoretical basis and technical reference for extracting crop canopy multispectral images.