Name
Title | ||
|---|---|---|
The early warning and prediction method of flea beetle based on maximum likelihood algorithm ensembles |
Abstract | ||
|---|---|---|
The forecast of vegetable plant diseases and insect pests commonly bases on experts' knowledge of plant protection while math modeling methods are scarcely used to analyze the associated data quantitatively. This paper establishes the forecast model for vegetable pest flea beetle by maximum likelihood algorithm. Besides, algorithm ensembles can improve the system of generalization learning ability, maximum likelihood algorithm ensembles can reduce the number of training samples taken on requirements. The experimental results of Guangdong vegetable pest flea beetle shows that the forecast accuracy of maximum likelihood algorithm ensembles provides a higher accuracy rate than that of nearest neighbor clustering, k-means clustering and support vector machine in the same condition. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICNC.2010.5584642 | ICNC |
Keywords | Field | DocType |
maximum likelihood algorithm ensembles,flea beetle,prediction method,pattern clustering,nearest neighbor clustering,forecast,ensembles,learning (artificial intelligence),maximum likelihood estimation,generalization learning ability,k-means clustering,insect pests,plant protection,agricultural engineering,crops,support vector machine,maximum likelihood,vegetable plant disease forecasting,pest control,support vector machines,early warning method,learning artificial intelligence,k means clustering,prediction algorithms,early warning,accuracy,nearest neighbor,classification algorithms,agriculture | k-nearest neighbors algorithm,Warning system,k-means clustering,Maximum likelihood algorithm,Computer science,Support vector machine,Flea beetle,Artificial intelligence,Statistical classification,Cluster analysis,Machine learning | Conference |
Volume | ISBN | Citations |
4 | 978-1-4244-5958-2 | 1 |
PageRank | References | Authors |
0.43 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ting Li | 1 | 1 | 0.43 |
| Jingfeng Yang | 2 | 61 | 8.34 |
| Zhimin Chen | 3 | 39 | 7.93 |