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In order to improve the effect of precision intelligent agricultural decision support system, this paper combines big data technology to carry out precision mining of agricultural data, and combines decision tree algorithm to carry out data classification processing. Moreover, this paper obtains the most effective agricultural decision reference data through data mining, combines the agricultural decision support process to set the functional modules of the decision system, and analyzes the implementation process of each functional module. In addition, this paper studies the theoretical basis and key technologies of the agricultural production structure optimisation decision support system, and builds a precision and intelligent agricultural decision support system based on big data analysis. The system mainly performs accurate processing of agricultural data and makes effective predictions, and then makes scientific decision results. Finally, this paper verifies the structure of the model in this paper combined with experimental analysis. From the comparison of experiments, it can be seen that the precision and intelligent agricultural decision support system constructed in this paper has significant effects. [ABSTRACT FROM AUTHOR]

In the years 2002–2005, special trials concerning the level of infection of pea varieties by downy mildew were performed in Poland. In these trials, the large number of varieties were tested in many locations (environments), separately on reach and light soils. Obtained trial data are unique because of the large scale of the performed investigations and also for the fact that all the observations were made by the same observer. In a paper, two methods of statistical analysis of such (ordered) data are compared. Several models have been proposed for the statistical interpretation of ordinal data. One of the most popular is the cumulative-type fixed logistic model. In the present work, using two field pea data sets, we considered whether adding random effects to the simple logistic model can improve inference. It was investigated whether there is any difference between the decisions concerning varieties resulting from the simple logistic model and the proposed mixed logistic model. The two models were also compared in terms of goodness of fit. According to two applied goodness-of-fit statistics, the mixed model performed better in all the cases. Statistical analysis (what is important for practical agriculture) enabled identification of the most resistant and the most susceptible variety from the analyzed set of cultivars. [ABSTRACT FROM AUTHOR]