With the continuous development of ground remote sensing technology, more and more crop canopy spectral sensors are applied to agricultural production, among which the Greenseeker plant spectral detector is widely used. Greenseeker can obtain crop canopy spectral information, normalized vegetation index (NDVI) data and divide fertilization management zoning. Targeted variable rate fertilization can be realized according to fertilization management zoning. The fuzzy c-means (FCM) algorithm is common for dividing fertilization management zoning, but the FCM algorithm has certain limitations. In the calculation process, the iterative calculation will be carried out continuously with the increase of data, which will affect the speed of fertilization management zoning. Based on the FCM algorithm, a model-based fuzzy c-means (MFCM) algorithm is proposed. In dividing the fertilization management partition, this algorithm does not have to iteratively calculate all the data every time a group of data is obtained, which can improve the speed of dividing the fertilization management partition. The NDVI data of soybean and maize were obtained through the established crop canopy spectral information collection platform. The fertilization management zoning was divided by the MFCM algorithm, and the division effect was evaluated by evaluation index contour coefficient (SC) and adjusted rand index (ARI). The results show that with the increased NDVI data, the MFCM algorithm can partition fertilization management partition faster than the FCM algorithm. The MFCM algorithm is 0.02~0.15 seconds faster; the MFCM algorithm is 0.07~0.51 seconds faster in dividing maize fertilization management zoning. By calculating the indexes SC and ARI to evaluate the effect of dividing fertilization management zoning, it is found that when dividing different NDVI data, the maximum difference of SC value is 0.022, indicating that the effect of dividing fertilization management zoning by the two algorithms is not different; The ARI value is sensitive to data changes. It can be maintained above 0.7 after the NDVI data volume reaches 6 000, but it will decrease significantly when the NDVI data changes.