The Soil Nutrient Spatial Interpolation Algorithm Based on KNN and IDW
For breaking the limitation of the GIS platform and realizing the soil nutrients spatial interpolation algorithm for any points in the monitoring area to transplant to the mobile platforms, this paper established the spatial index of the soil nutrient sam
- PDF / 658,612 Bytes
- 13 Pages / 439.37 x 666.142 pts Page_size
- 22 Downloads / 188 Views
2
College of Information and Management Science, Henan Agricultural University, Zhengzhou 450002, China [email protected] Henan grain crops Collaborative Innovation Center, Zhengzhou 450002, China 3 Ministry of Agriculture Agricultural Information Technology Science Observation Station, Huanghuaihai District, Zhengzhou 450002, China
Abstract. For breaking the limitation of the GIS platform and realizing the soil nutrients spatial interpolation algorithm for any points in the monitoring area to transplant to the mobile platforms, this paper established the spatial index of the soil nutrient sampling points utilizing the K-D Tree as the space splitting algorithm of the soil nutrient sampling points. On this basis, the K nearest neighbor search of the soil nutrient sampling points was also implemented employing KNN algorithm. Finally, the soil nutrient spatial interpolation was realized based on KNN and IDW algorithm. Meanwhile, the accuracy of the algorithm and the influence to the different soil nutrient elements affected by the K value in KNN algorithm were also verified. The results show that the soil nutrient spatial interpolation algorithm was viable to predict the element contents of soil nutrient during the running time was less than 3 s. To reach the best accuracy, the values of the proximal point K for predicting the PH, organic matter, rapid available phosphorus and rapid available potassium should be set as 85, 15, the largest sample space and 65 respectively. The optimal average absolute error of the pH, organic matter, rapid available phosphorus and rapid available potassium was 0.0405, 0.3870, 0.0015 respectively. Keywords: IDW
KNN KD-Tree Soil nutrient Spatial interpolation
1 Introduction Due to the problems of the obtaining the sampling points data of the soil testing and formulated fertilization such as difficult sampling [1], heavy workload [2], high cost [3] and so on, the data points during the fertilizing decision stage in the process of testing soil for formulated fertilization was not enough to achieve the complete coverage for the field parcel in each region. Therefore, the sampling point soil nutrient data of the currently unknown spatial points were obtained on the basis of interpolation operation of the existing sampling point data frequently in real application. The utilizing of spatial interpolation technology was commonly used method. In the method, the statistical methods were applied to the nutrient data of some smaller density soil sample © IFIP International Federation for Information Processing 2016 Published by Springer International Publishing AG 2016. All Rights Reserved D. Li and Z. Li (Eds.): CCTA 2015, Part I, IFIP AICT 478, pp. 412–424, 2016. DOI: 10.1007/978-3-319-48357-3_40
The Soil Nutrient Spatial Interpolation Algorithm
413
points to conduct interpolation operation to the data from the points that were not sampled to form the more dense point data distribution or the area data of different areal unit. The soil nutrient values of the points which were not sampl
Data Loading...
