Multi-resolution pruning based co-location identification in spatial data

Computer technologies have been introduced into area of agriculture recently. Many research work has been carried out on the field of spatial data mining. A spatial data is the data dealing with location anywhere on earth. A co-location spatial pattern consist of multiple groups, which co-relates spatial features or events that are frequently located in same zone. In existing system uses the concept of probabilistic prevalent co-location mining, it can find co-location that are likely to be prevalent but it needs more efficiency it can be improved. This paper focuses on spatial co-location mining in agriculture. Novel multi-resolution pruning technique can be used to address the problem of mining co-location data patterns with rare spatial features. Combinatorial spatial co-location mining algorithm can be used to find locality, provide their soil information and display its crop varieties.