摘要
为了提高农业管理水平,将计算机智能技术与农业技术相结合,提出基于改进的SVM算法建立标准农田地力等级的评价模型,在评价模型中利用频繁闭集挖掘算法获取特征向量集合,再利用SVM算法建立耕地地力评价模型。仿真结果表明:评价结果符合当地实际情况,并且与传统的评价模型相比,该模型对非线性特征值评价评价中精确度更高。
Abstract： In order to improve the level of management of agriculture and combined with artificial intelligence, a standard evaluation on soil fertility grade for cultivated land was made based on improved-SVM. This model used data mining algorithm of FCIs to obtain the list of feature vectors, then the evaluation model of soil fertility grade for cultivated land was established by SVM algorithm. The experiment result was basically consistent with the actual status, status, showed that the model is better practiocable and higher accuracy for evaluation of the nonlitlear characters.
出处
《沈阳农业大学学报》
CAS
CSCD
北大核心
2012年第1期126-128,共3页
Journal of Shenyang Agricultural University
基金
广西省自然科学基金项目(桂科青0832101)
关键词
耕地地力
支持向量机
评价模型
productivity of cultivated land
support vector machine
evaluation model
