• Kusum Lata
• Bhushan Chaudhari

In the era of internet and technology agriculture field of study requires attention in order to equip the farmers to maximize their output. In our country agriculture is the strength of the economy and growth and more than half of the population is living on the agriculture output. The crop yield is the major factor to decide the farmers earning and governments planning to meet the requirements to ensure the food security. Crop yield prediction will assist the farmers and other stakeholders for better crop planning i.e. selling, warehousing, market prices etc. Mainly data mining techniques for DSS is based on artificial neural networks, Bayesian networks, vector support system etc. There are various researchers working on this area and proposed several techniques to attain the accuracy for crop yield, but the utmost accuracy and error free information is still need the enhancement to extract data from the bigger data sets. The decision support system will help the farmers to cut the losses, farmer suicides and also will improve the crop yield due to proactive planning. This paper discusses and compares the various data mining techniques available for the decision support systems i.e. crop yield prediction.

Decision Support System, Prediction, Accuracy, Mining, Crop Yield Prediction

"CROP YIELD PREDICTION USING DATA MINING TECHNIQUES AND MACHINE LEARNING MODELS FOR DECISION SUPPORT SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.391-396, April-2019, Available :http://www.jetir.org/papers/JETIR1904H66.pdf

"CROP YIELD PREDICTION USING DATA MINING TECHNIQUES AND MACHINE LEARNING MODELS FOR DECISION SUPPORT SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp391-396, April-2019, Available at : http://www.jetir.org/papers/JETIR1904H66.pdf