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Based on the meteorological data collected from 3 stations in recent 10 years in Fushun region, the reference crop evapotranspiration was calculated with the Penman-Monteith equation recommended by FAO in 1990. The evapotranspiration of the region was analyzed using linear regression analysis based on software of SPSS. The results showed that the linear correlation was evident between the evapotranspiration data of stations, but it is not significant for the linear correlation between the stations, neither between the stations. The result will be helpful to analysis of the space distributions of the reference crop evapotranspiration in the region.

Take the test zone 110 sampling plots of soil nutrient content as the research data, to study the correlation between the soil nutrient content and the corn yield. It used the grey system theory to analysis the correlation between the content of soil nutrient, and established GM (1, N) prediction model. Soil nutrient correlation degree analysis showed that the correlation coefficient of available phosphorus and available potassium were 0.4742, 0.4492, it has a significant effect on the corn yield, the average prediction error of the GM (1, N) model was 7.38%. The model reflected the relationship between soil nutrient content and the corn yield well; it can be used to predict the yield test area.

Taking the grain yield data from 1980 to 2012 of Jilin Province for example, this paper analyzes the main factors that influences the grain yield based on the principle component analysis method. According to these main factors, the input samples of BP neutral network are definite. Thereby, the BP neutral networks could be trained to predict. The results show that the fertilizer consumption, large cattle head number, end grain sowing area, effective irrigation area and rural per capita living space are the main effect factor on grain yield. The BP neural network was built by using it as the input samples. The number of input nodes of the network is determined. Then build the prediction model of grain production in Jilin province. The simulation results show that, the average error of prediction results of BP neural network model based on principal component analysis is 4.48%.

According to the difference in grain production capacity of the region in Jilin province, to formulate the strategy of sustainable development of grain production in Jilin province, to provide scientific basis for related departments to establish food production decision. In this paper, cluster analysis of multivariate statistical method was used, analyzed the regional difference characteristics of grain production capacity of 25 regions of Jilin Province in 2012, and elaborated the reasons for the formation of regional differences. Through cluster analysis, the 25 area of Jilin province is divided into 4 categories. Several factors of regional location, quantity of cultivated land area and population, agricultural modernization is the difference in the main area of grain production capacity in Jilin Province.

The research of time series analysis model to predict the effect of rice blast, provides the reference for the prediction of the disease. In successive years of planting area and the rice blast incidence area as the original data,were used to establish the forecast model of time series analysis of moving average, exponential mean and variance analysis of periodic extrapolation of rice blast incidence, analysis its forecast results, and built at the beginning ofthe prediction was verified in application. The experimental results show that the time sequence analysis model can be used for long-term forecasting of rice blast.

Nowadays, there is a variety of methods on soil nutrient environment prediction, in order to improve the accuracy of prediction data, more than prediction model can be used. The Jilin province is an important agricultural production area in China. The analysis of the soil nutrition environment is an important theoretical basis for agricultural planting structure. It is play important role to make the food security and agricultural sustainable development strategy. The research results show that the optimal partition number is 8 kinds of nutrient types. From the research results, it can be found the method of principal component and cluster analysis can be successfully applied to classify soils in Jilin province.Not only the partition results can guide sampling, but also provide an important method for accurately predicting the soil nutrition environment in agricultural products.The results can be used to implement variable input and precise fertilization recommendation.

The precision agriculture (PA) is the end product of the modern high tech's information technology and the agricultural production technology union. The soil nutrients are not only an important component of soil research, but also a critical determinant of its productivity. Some soil nutrients spatial distribution pictures were established based on important factors that affect crops production. The soil nutrient situation was understood by the paper in Jilin province black soil. The results can realized some information opening and sharing and helped some farmer and manager to understand some soil nutrient spatial distribution. They can speed up the development of Jilin province’s precision agriculture.

This research used the method of rough set and decision tree in data mining, building the evaluation model of soil fertility, to evaluate NongAn of Jilin province farmland productivity. Results show that the NongAn of arable land is divided into six grades, the fertility of grade one to six account for 6.77%, 14.29%, 23.78%, 23.51%, 20.13%, and 11.54% respectively. The farmland productivity difference is significant. The evaluation model of the evaluation results are in conformity with actual farmland productivity NongAn, avoiding the evaluation differences caused by the subjective factor, can effectively improve the soil fertility level classification accuracy and objectivity.

precision agriculture, soil fertility evaluation is the foundation of variable rate fertilization, the initial clustering centers of K means algorithm soil fertility levels in the traditional evaluation methods generated randomly from the data set, the clustering result is not stable. This paper proposes an improved K-means algorithm density algorithm to optimize the initial clustering center selection algorithm based on K, the most far away to each other in high density region point as the initial cluster center. Experiments show that, the improved K-means algorithm can eliminate the dependence on the initial cluster center; the clustering result has been greatly improved.

In order to realize the real-time monitoring of crop growth environment, this paper applied the Java JDK 6 + Eclipse 3.5, developed agricultural remote data collection system based on zigbee, greenhouse planting of crops growth environment parameters, such as air temperature, humidity and so on, with all kinds of sensor dynamic collection, and transmitted to the user through the network client terminal. This makes agricultural personnel through the terminal computer real time control of the growth environment of crops, can take timely control measures, has realized the remote, multiobjective agricultural information, the parameters of the real-time acquisition, display, storage, query, statistics, and other functions, provides a effective way for the agricultural informatization construction.

Variance analysis is a method commonly used in biological statistics. How to use the statistical analysis software for the analysis of variance to achieve the results of the rapid and scientific treatment, to obtain the correct conclusion, is an important part of agricultural research. This paper introduces how to use SPSS (Statistical Package for the Social Science or Statistic Products andService Solution) data analysis tool for the variance analysis method; the data analysis and processing of fast, accurate and intuitive; compared with Excel, the statistical analysis function of SPSS is more powerful, can help to improve the efficiency of data processing, but also reduce the experimental cost.

This paper mainly analyzes the effects of solar storms on GPS measurement errors. Solar storms produce a lot of charged particles, which makes the electron density in ionosphere increase, and in further affects the ionosphere delay errors. Ionosphere delay errors are one of the main sources of GPS measurement error, which is a direct impact on measurement precision of GPS. GPS distance errors are caused by ionosphere delay errors. In the case of solar storms, dual frequency observation cannot completely remove ionosphere delay, so in measurement process appropriate methods will be used to reduce errors in GPS measurement, for example, during the observation in the night and increased observation period. By comparing and analyzing the GPS observation data in solar storms and under normal conditions with GAMIT/GLOBK software, a conclusion is made in this paper that observing in the night and the increased observation period can reduce the GPS measurement errors.

According to the problems of programming courses in the teaching process, this paper proposes the "two way, three classrooms, four reform, multiple assessment" teaching model of C++ programming combining with some teaching experience, and researches and discusses from programming course content, teaching methods and assessment methods. The teaching model applied in other programming course, and obtained the same good results.