Method for zoning corn based on the ndvi and the improved som-k-means algorithm

To solve problems such as low nitrogen use efficiency during corn intertillage and topdressing and the presence of spatial differences in corn growth, a method for zoning and division based on the normalized difference vegetation index (NDVI) and the improved self-organizing map (SOM)-K-means algorithm was proposed. First, the GreenSeeker spectrum sensor was utilized to acquire the NDVI of the corn canopy in the vegetative V6-V10 stage during the intertillage period. Second, the acquired data were screened, and the SOM-K-means algorithm was used to perform a cluster analysis of the processed data. Finally, the clustering performance was analyzed. The initial clustering center was acquired with an SOM neural network, the clustering center was used in K-means clustering, and zoning was performed. The optimal number of zones was 4 according to the Davies-Boulding Index (DBI), the silhouette coefficient, and a silhouette analysis of the differences in corn growth. With four zones, the DBI and the silhouette coefficient were 0.569 and 0.537, respectively. Clusters 1 and 4 and clusters 2 and 3 in the silhouette map displayed similar thicknesses, with large differences between clusters and small differences within clusters. A comparison of the SOM-K-means algorithm, K-means algorithm, and SOM neural network algorithm revealed that the run time of the SOM-K-means clustering algorithm was 4.880 s, its inertia was 9.2118, and the run time of the SOM neural network was 4.715 s. The overall coefficient of variation of corn growth in the unzoned test area was 15.24%, and the overall coefficient of variation of corn growth after zoning was 6.94%. This method provides a new approach for understanding issues related to variable-rate fertilization zoning and online real-time zoning during corn intertillage based on the NDVI.