Spatial interpolation of soil nutrients using algebra hyper-curve neural network

Abstract

Spatial distribution of and interpolation methods for soil nutrients are the basis of soil nutrient management in precision agriculture. For study of application potential and characteristics of algebra hyper-curve neural network(AHCNN) in delineating spatial variability and interpolation of soil properties, 956 soil samples were taken from a 50 hectare field with 20 m interval for alkaline hydrolytic nitrogen measurement. The test data set consisted of 100 random samples extracted from the 956 samples, and the training data set extracted from the remaining samples using 20, 40, 60, 80, 100 and 120 m grid intervals. Using the AHCNN model, three training plans were designed, including plan AHC1, using spatial coordinates as the only network input, plan AHC2, adding information of four neighboring points as network input, and plan AHC3, adding information of six neighboring points as network input. The interpolation precision of AHCNN method was compared with that of Kriging method. When the number of training samples was big, interpolation precisions of Kriging and AHCNN were similar. When the number of training samples was small, the precisions of both methods deteriorated. Since AHCNN method has no request on data distribution and it is non-linearization of neutron input variables, it is suitable for delineation of spatial distribution of nonlinear soil properties. In addition, AHCNN has an advantage of adaptive self-adjustment of model parameters, which makes it proper for soil nutrient spatial interpolation. After comparison of mean absolute error d, root mean squared error RMSE, and mean relative error d%, and the spatial distribution maps generated from different methods, it can be concluded that using spatial coordinates as the only network input cannot simulate the characteristics of soil nutrient spatial variability well, and the simulation results can be improved greatly after adding neighboring sample points’ information and the distance effect as network input. When the number of samples was small, interpolation precision can be improved after properly increasing the number of neighboring sample points. It was also showed that evaluation of interpolation precision using conventional error statistic indexes was defective, and the spatial distribution map should be used as an important evaluation factor.

Keywords: algebra hyper-curve neural network (AHCNN), spatial interpolation, soil nutrients, spatial variability, Kriging
interpolation
DOI: 10.3965/j.issn.1934-6344.2008.01.051-056
Citation: Chen Liping, Zhao Chunjiang, Huang Wenqian, Chen Tian’en, Wang Jihua, Liu Zhenyan, et al. Spatial interpolation of soil nutrients using algebra hyper-curve neural network. Int J Agric & Biol Eng. 2008; 1(1): 51