Delineating Management Zones in Maize Field Based on Fuzzy C-means Algorithm

Taking the demonstration area of drip irrigation under film as the research object, and aiming at delineating management zones in large areas of farmland, in Huangyanghe Farm, Gansu Province. The topographical attributes (elevation slope and aspect), soil texture (sand, clay and silt), soil moisture content, available nitrogen, electrical conductivity and yield of maize were considered, the degree of variation and correlation of each factor were analyzed, and then the master factors of maize yield were extracted by the results of correlation analysis. Three principal components were obtained by principal component analysis (PCA) based on the master factors. Fuzzy c-means clustering algorithm (FCM) was used to delineate management zones based on the spatial variation of the principal components, the optimal partition number was determined by the fuzzy performance index (FPI), and normalized classification entropy (NCE) were minimum at the same time, and then the differences of the master factors among the management zones were analyzed. Results showed that the master factors were silt, sand, soil water content, available nitrogen, electrical conductivity and elevation, and three management zones were determined by FCM. Statistically significant differences in the master factors were found among the three management zones. Soil water content, crop height and LAI were also significantly different in different management zones during the crop growth period. The spatial variation of the factors within the same management zones was smaller than that of the factors in the whole field, and the variation between zones was large. Delineation of management zones should be adopted based on the characteristics of each zone, and the research result provided a theoretical basis for the implementation of precision agriculture. © 2019, Chinese Society of Agricultural Machinery. All right reserved.