| Abstract | Lately the erratic weather conditions changes have prompted different polemics. The utilization of
cropping designs that have been done from generation to generation regardless of environmental change and the
environment is the reason. The advances in computing and information storage have given tremendous
measures of data. The test has been to remove knowledge from this raw data that has lead to new strategies and
techniques, for example, data mining that can connect the knowledge gap. This research expected to evaluate
these new data mining techniques and apply them to soil nutrients and weather database to layout in the event
that significant connections can be found. So in this paper we proposed Hybrid Artificial Neural Network and
Case Based Reasoning (HANNCBR) Classification Model for conclude the best crop to be cultivated
considering the factors, the soil’s mineral substance proportions and weather patterns. The proposed Hybrid
Classification Model (HANNCBR) is contrasted and Naïve Bayes and SVM. The proposed Hybrid
Classification Model (HANNCBR) performs well other than existing methodologies.
Keyword: Data mining, soil nutrients and weather database, Hybrid Artificial Neural Network, Case Based
Reasoning and Classification;
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