Question3: What are the benefits of earlier prediction of crop yield?Observed Benefits are – 1.  It helps in taking decisions.2.

  It also assists in identifying the relevance of attributes which significantly affect the crop yield.3. It helps in defining a decision support system (DSS) for whole farm management with the goal of optimizing returns on inputs while preserving resources. 4. It helps in Agronomic Management which is the most important input for getting potential yield and high net returns in any crop or crop sequence.

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5.  It helps in maximize the crop yield by selection process of the appropriate crop that will be sown plays a vital role. Question4: What are the applications of Data Mining Techniques in field of agriculture?There are several applications of Data Mining techniques in the field of agriculture. Some of the data mining techniques are related to weather conditions and forecasts. For example, the K-Means algorithm is used to perform forecast of the pollution in the atmosphere, the K Nearest Neighbor (KNN) is applied for simulating daily precipitations and other weather variables, and different possible changes of the weather scenarios are analyzed using SVMs.        3. Conclusion & Future WorkThis paper reviews different systems specialized accomplishments in the field of crop yield prediction. Discuses methodology, comprehensive survey of various proposed methods to predict crop yield and applications.

It also discusses various data mining techniques used for prediction of crop yield. Growing better strategies to foresee crop productivity in various climatic conditions can help farmer and different partners in essential basic leadership as far as agronomy and product decision. It additionally talks about different data mining methods utilized for prediction of crop yield. Developing better strategies to predict crop profitability in different climatic conditions can help rancher and distinctive partners in basic fundamental leadership to the extent agronomy and item choice. It also helps the farmers to merchandise the products without middlemen which help them to obtain maximum price for their products.

Promote the  investigation results can be specifically accessible to farmers through web which can expand generation and can improve price. Performance of these classification algorithms can be evaluated on some bigger test datasets in order to get better results 4. References1    Kalyani, M.R.

(2012) “Data Mining Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering, 10(2):439-442.2    Agrawal, H., Agrawal, P (2014)., “Review on Data Mining Tools”, International Journal of Innovative Science, Engineering & Technology, 2(1) :52-56. 3    Grajales, D.F.P., Mosquera, G.

J.A, Mejia, F., Piedrahita, L.C. and Basurto, C .

(2015) “Crop-Planning, Making Smarter Agriculture With Climate Data” International Conference on Agro-GeoInformatics:240-244. 4    Bendre, M. R., Thool, R.C., Thool and V. R.

(2015) “Big Data in Precision Agriculture : Weather Forecasting for Future Farming”, International Conference on Next Generation Computing Technologies:744-750.5    Hemageetha, N(2016) “A survey on application of data mining techniques to analyze the soil for agricultural purpose”, International Conference on Computing for Sustainable Global Development (INDIACom):3112-3117.6    Fathima, G.N., Geetha, R.(2014) “Agriculture Crop Pattern Using Data Mining Techniques”, International Journal of Advanced Research in Computer Science and Engineering, 5(4):781-786.

7    Rub, G.(2014) “Data Mining of Agricultural Yield Data:A Comparison of Regression Models”, Industrial Confrence:24-37. 8    Raorane, A.A.

, Kulkarni R.V(2012) “Data Mining: An effective tool for yield estimation in the agricultural sector”, International Journal of Emerging Trends & Technology in Computer Science(IJETTCS), 1(2):75-79.9    Veenadhari, S., Misra, B and  Singh, C.

D(2014) “Machine learning approach for forecasting crop yield based on climatic parameters”, International Conference on Computer Communication and Informatics:1-5.10    Sujatha, R., Isakki, P(2016)”A study on crop yield forecasting using classification techniques”, International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE):1-4.11    Kushwaha, A.K., SwetaBhattachrya(2015) “Crop yield prediction using Agro Algorithm in Hadoop”, International Journal of Computer Science and Information Technology & Security (IJCSITS), 5(2)271-274.12    Sellam,V., Poovammal(2016)  E.

, “Prediction of Crop Yield using Regression Analysis”, Indian Journal of Science and Technology, 9(38):1-5.13    Kaur, M., Gulati, H., Kundra, H(2014) “Data Mining in Agriculture on Crop Price Prediction: Techniques and Applications”, International Journal of Computer Applications,  99(12), 1-3.14    Ramesh and Vijay.

S (2014) “A survey on Data Mining Techniques for Crop Yield Prediction ” International Journal of Advance Research in  2( 9); 2321- 7782.15    Ms.S.Shenbagavadivu, , Ms.P.

Kanjana Dev(2016) ” Enhanced Crop Yield Prediction and Soil Data  Analysis Using Data Mining “International Journal of Modern Computer Science (IJMCS) 4( 6); 2320-7868 16    Dakshayini Patil , Dr.P.G.Halakatti (2017) “Rice Crop Yield Prediction using Data Mining Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering 7(5); 427-431.17    Rupinder Singh , Gurpreet Singh (2017)”Wheat Crop Yield Assessment Using Decision Tree Algorithms”, International Journal of Advanced Research in Computer Science, 8 (5);1809-1817. 18    E.

Manjula , S. Djodiltachoumy(2017)  “A Model for Prediction of Crop Yield” International Journal of Computational Intelligence and Informatics, 6( 4);2349-6363.19     Marzieh Mokarram, Mohammad Jaafar Mokarram and Behrouz Safarianejadian(2017) “Using Adaptive Neuro Fuzzy Inference System (ANFIS) for Prediction of Soil Fertility for Wheat Cultivation “Biological Forum – An International Journal  9(1): 37-44.

20    Menaka .K, N. Yuvraj (2017) “ANFIS Based Crop Yield Prediction Model” International Journal of Science, Engineering and Technology Research (IJSETR) ,6(5); 2278 -7798


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