Establishment of castor core collection utilizing Self-Organizing Mapping (SOM) networks. (Special Issue: Artificial intelligence in agriculture.)

摘要

A core collection can be defined as a representative sample of entire germplasm collection with minimum repetitiveness and maximum genetic diversity of a crop species and its relatives. The success of development of a most representative core collection mainly depends on non-overlapping grouping of whole collection. In the present study, a promising method viz., Self Organizing Mapping (SOM) network clustering technique was applied, which was first time attempted in establishment of core collection in a crop species. An attempt was made to compare SOM with clustering methods viz., Ward's and K-means clustering to understand the superiority of SOM over these two methods in forming castor core representative of whole collection. Forty experimental cores were constructed using these clustering methods as well two clustering algorithms (single and two stage) and two allocation methods, viz., proportional and logarithmic methods. Three sample sizes representing 10 per cent, 15 per cent and 20 per cent of total collection were drawn, and a fourth sample size of 524 accession based on progress was made. Thus formed experimental cores were evaluated based on the four parameters viz., mean difference percentage (MD), variance difference percentage (VD), coincidence rate percentage (CR) and variable rate percentage (VR). The results indicated that SOM method performed better as compared to Ward's and K-means clustering methods conserving maximum diversity existing in the whole germplasm collection.