Research On Optimization Algorithm Of Farmland Soil Sampling Layout

Precision fertilization can achieve balanced fertilization for different crops in different soil by obtaining soil nutrient content.However,a large number of soil nutrient collection and testing will increase the planting cost of crops.Therefore,it is necessary to divide the sampling grid reasonably and reduce the number of samples.The advancement of science and technology has driven the technological development of various industries,and the application of data mining technology in various industries has become increasingly popular.The main application fields are various traditional industries,financial industries,and IT industries.The combination of data mining and precision fertilization technology is currently the focus of this thesis.In the process of crop cultivation,fertilization and fertilization,if targeted and reasonable implementation of nutrients on crops,it can more effectively promote the growth of crops,and at the same time,it may make greater contributions to the issue of conservation of nutrients and environmental protection.Aiming at the problem of precise fertilization,data mining technology can re-cluster and divide soil units based on samples of soil nutrients.Based on the results of the division,farmers can re-sample according to the new soil blocks,and then perform targeted Fertilize.In this way,the number of samples can be greatly reduced,so that the cost of sampling and testing is reduced,and this precise fertilization scheme can be popularized in various places.This article first uses the gray model to predict the soil nutrient content,and takes into account the impact between the various objects in the actual space.In response to the lack of spatial information in the model,the spatial autocorrelation coefficient is added.The average accuracy rate of prediction reached 87.2%,and then the K-means algorithm based on hierarchical analysis in cluster analysis was used to realize the clustering of each sampling point according to the N,P,and K content of the sampling point in the soil.The sampling unit is merged into a new unit,so the original sampling layout is re-divided,and the number of sampling points is effectively reduced.When the sampling is performed again,sampling can be performed according to the new layout,and the effect can be achieved.The data collection in this study is to obtain the nutrient content information in the soil through the support of GIS technology.The four-year sampling results of the experimental field of No.13 Village,Gongpeng Town,Yushu City,Jilin Province are predicted and the optimal layout of the samples is obtained.The new sampling results form a new topographic distribution map of the sampling layout.In the end,the sampling cost is greatly reduced and the farmland is accurately fertilized.During the experiment,the data mining statistical function of R language was mainly used to optimize the sampling point layout of the prediction result,and then compared with the actual value sampling point layout optimization.Through comparative analysis,it can be seen that the prediction result has high accuracy,and the difference between the prediction result and the clustering optimization result of the actual value is small.Therefore,the algorithm is of high value to the actual reference and can provide a reference basis for relevant departments.