• N. HARSHINI
• Dr. M. RATHAMANI

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.

Data mining, soil nutrients and weather database, Hybrid Artificial Neural Network, Case Based Reasoning and Classification

"HYBRID ARTIFICIAL NEURAL NETWORK AND CASE BASED REASONING (HANNCBR) CLASSIFICATION MODEL FOR CLASSIFICATION OF AGRICULTURE DATA IN DIFFERENT AREA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.b123-b132, November-2023, Available :http://www.jetir.org/papers/JETIR2311118.pdf

"HYBRID ARTIFICIAL NEURAL NETWORK AND CASE BASED REASONING (HANNCBR) CLASSIFICATION MODEL FOR CLASSIFICATION OF AGRICULTURE DATA IN DIFFERENT AREA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppb123-b132, November-2023, Available at : http://www.jetir.org/papers/JETIR2311118.pdf