Research And Application Of Spatial Data Mining Technology For Precision Agriculture

Spatial data mining is the emerging multi-disciplinary cross-edge disciplines with the development of computer technology database applications and management decision support technology. With the growing popularity of 3S technology in agricultural and the rapid growth of agricultural data agricultural Information Science has become one of the richest opportunities and challenges of applications of the spatial data mining. This article discusses several key issues of spatial data mining and intelligent spatial decision-making that corn precision operation intelligent decision support system involved the process of the implementation of the national 863 project of corn precise operating system research and applications and presents a space-based data mining of the corn precision operation intelligent spatial decision support system and from the theory and practice of the system to explore research and application and carries out exploratory research and application from the theory and practice. Based on requests of fertility database and corn precision operation using of spatial data mining this paper presents a new method to solve soil fertility corn precise fertilization soil fertility level and yield forecasting. The research result has been successfully applied to maize precise operating intelligent decision-making system. Major work and innovation points in the following areas:1. For the Spatial cluster research present situation of spatial clustering and the actual needs of precision agriculture intelligent decision-making in order to solve regional problems of corn precision fertilizer corn precision fertilization based on spatial fuzzy clustering algorithm has been researched. Using fuzzy clustering analysis method to study the classification of soil nutrient studies and establish classification model using 8-connected to carry on spatial clustering analysis and the results of fuzzy clustering applied to spatial clustering. This two-stage clustering method is superior to the traditional single-stage clustering. It has some significance for spatial data mining and theoretical studies. The main contribution of this approach is that :(1) Spatial fuzzy clustering method for the first time applies to soil nutrient content analysis; (2) The existing clustering algorithms are simple non-spatial attribute clustering or spatial location based on nearby clustering but for precision agriculture in the precise fertilization must also consider the attributes and spatial location combination of the two clustering algorithms have not been reported previously. This classification is in line with the needs of corn precise fertilization. The classification has an important guiding significance for corn precision fertilizer and other field management and operation of precision agriculture to do decision making so make precision agriculture toward a more practical direction.2. Spatial fuzzy clustering algorithm based on weighted has been proposed application of weighted spatial fuzzy dynamic clustering algorithm can be able to conduct more accurate evaluation of soil fertility. For variable rate fertilization on soil nutrient study on the impact common practice is to compare soil N P K and other nutrients and do not comprehensive consideration of various nutrients in the soil with each other. There is also a part of the study of variable rate fertilization effects on soil nutrients. This paper presents an improved spatial fuzzy dynamic clustering algorithm and its application in soil fertility evaluation in view of traditional spatial fuzzy clustering algorithm insufficiency.The specific process including: first obtains various attribute weight using the analytic hierarchy process then unifies the weight and the spatial fuzzy dynamic cluster law in the end use F distribution in probability statistics to determine the best classification to improve the spatial fuzzy clustering algorithm for intelligent. Compared the method of the paper with the transitive closure method based on fuzzy equivalence relation.The experimental results show that: the algorithm clustering accuracy rate is significantly higher than the weighted fuzzy clustering algorithm. The improved algorithm is applied to soil fertility evaluation of precision agriculture the experimental results is in line with the actual situation. It makes a contribution to the accurate evaluation of soil fertility.3. Research on the optimization algorithm based on rough set– decision tree and application on soil fertility evaluation. Looking from data mining's angle the soil evaluation materially belongs to the classified forecast question. Decision tree method is a better classification method suitable for dealing with nonlinear data and classification and predicted decision-making problems of describe data the speed of production decision tree is quick easy to extract the rules easy to understand. When the attributes in data set are excessively more some situations is easy to be appear when classifies with the decision tree such as decision tree structure is not good discover some useful rules information is difficulty and so on.In the rough set theory knowledge is interpreted as a variety of division on the domain model using two precision approximation and lower approximation sets to approximate the collection to be described is suitable for dealing with uncertain knowledge. Rough set theory can support data mining and knowledge discovery in multiple steps such as data preprocessing data reduction rule generation data acquisition and other dependencies. So rough sets and decision tree have a strong advantage of complementarities. This article uses the clustering method to obtain samples and the samples is more typical checked by decision tree model so the samples can be used as a standard model of learning samples; using rough set attribute reduction method combined with decision tree method to evaluate the productivity grade of soil that is use the rough set method to reduce the land attributes and obtain a low-dimensional training data then use decision tree method to build decision tree obtain classified rule sets. This method not only reduces the tree's branches removing redundant attributes improve mining efficiency; and also enhance the model accuracy. The algorithm can effectively improve the soil fertility level classification accuracy and objectivity. And provides new ideas and methods for spatial data mining and knowledge discovery.4. Use of time series algorithm to predict corn yield has been studied corn production forecast is an important part of the implementation process of precision agriculture the traditional statistical methods are relatively large errors the cost of production forecast application of remote sensing maps is relatively high. In this paper time series algorithm method of sliding summation autoregressive (ARIMA) is used to predict the yield of maize and makes up for the shortcomings of statistical methods big errors but also remotes sensing measurements to verify the results produced. Experimental results show that the predict corn yield application of ARIMA models is very good with the actual value fitting effect and indicate that time series algorithm can be used on maize production to better predict future trends. This has provided the new mentality and the method for the corn rate of prediction analysis.5. Corn precise assignment is a complex spatial decision-making tasks this article Uses artificial intelligence techniques spatial analysis methods statistical analysis methods digital maps image analysis and visualization methods and so on in-depth analysis space factors time factors that accurate operations variables affecting corn fertilizing spraying variables intelligent measuring production that impact on variable rate fertilization variable spraying intelligent measuring production in corn precision operation and design and implement the maize precise intelligent space decision-making system.The system will effectively integrate geographic information system with spatial information processing functions spatial data mining techniques with spatial information analysis capabilities expert system technology in the field of artificial intelligence traditional information management systems Decision Support System and combine the statistical analysis of GIS methods and data visualization thus enhancing the capacity that agricultural administrative department full use of various properties and spatial information data to make decisions.The actual application shows that the system is very suitable for precision agriculture intelligent decision-making and further enriched and perfected the practice-oriented field study of spatial data mining techniques and methods.