In India, farmers are not getting expected crop yield from their productions. Crop production mostly depends on weather conditions and some statistical methodologies. To get higher crop production yield, farmers sometimes need advices for predicting and analyzing future crop production. This helps farmers to produce a crop with maximum yield. Such methods will be helpful for farmers and government to make a better decision to increase crop production. In this paper present a review on crop yield prediction (CYP) with different data mining (DM) techniques used to evaluate and predict the problem lead to increase CYP. The result analysis is performed on root mean square error (RMSE) and peak signal noise ratio (PSNR).

RMSE, ANFIS, Data mining, Crop Yield, PSNR

[1] B Vishnu Vardhan and D. Ramesh, O Subhash Chander Goud “ Density Based Clustering Technique on Crop Yield Prediction” International Journal of Electronics and Electrical Engineering Vol. 2, No. 1, March, 2014
[2] Naushina Farheen Imam Shaikh , Prof. R. V. Argiddi “Annual Crop Yield Prediction and Recommend Planting of Different Crops by Using Data Mining Technique” International Journal of Innovative Research in Computer and Communication Engineering, Vol. 4, Issue 10, October 2016
[3] Chapman P. Gleason Large Area Yield Estimation/ Forecasting Using Plantprocess Models By Chapman P. Gleason For Presentation At The 1982 Winter Meeting Americansociety Of Agricultural Engineers Palmer House, Chicago, Illinois December R 14-17, 1982.
[4] Jiawei Han, Micheline Kamber, Jian Pie, “Data Mining Concepts and Techniques”, Morgan Kaufmann, ASIN B0058NBJ2M
[5] Raorane A.A., Kulkarni R.V. “Data Mining: An effective tool for yield estimation in the agricultural sector” International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) Volume 1, Issue 2, July – August 2012
[6] Applying Naive Bayes Data Mining Technique for Classification of Agricultural Land Soils P.Bhargavi, Dr.S.Jyothi, IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.8, August 2009
[7] SVM- Muller, K., et al. "An introduction to kernel-based learning algorithms.” Neural Network IEEE transactions on 12.2 (2001): 181-201
[8] Aliyu Muazu, Azmi Yahya, W.I.W. Ishak, S. Khairunniza-Bejo. Yield prediction modeling using data envelopment analysis methodology for direct seeding, wetland paddy cultivation. Agriculture and Agricultural Science Procedia. 2014; 2, 181-190.
[9] K. Menaka , N.Yuvaraj “A survey on crop yield prediction models” Indian Journal of Innovations and Developments Vol 5(12), October, 2016.
[10] Kadir, M. K. A., Ayob, M. Z., & Miniappan, N. (2014, August). Wheat yield prediction: Artificial neural network based approach. In Engineering Technology and Technopreneuship (ICE2T), 2014 4th International Conference on (pp. 161-165). IEEE.
[11] Keong, Y. K., & Keng, W. M. (2012). Statistical modeling of weather-based yield forecasting for young mature oil palm. APCBEE Procedia, 4, 58-65.
[12] Khoshnevisan, B., Rafiee, S., Omid, M., & Mousazadeh, H. (2014). Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs. Information processing in agriculture, 1(1), 14-22.
[13] M. Gunasundari Ananthara, Dr. T. Arunkumar and Ms. R. Hemavathy “CRY – An improved Crop Yield Prediction model using Bee Hive Clustering Approach for Agricultural data sets” International Conference on Pattern Recognition, Informatics and Mobile Engineering, IEEE, 2013.
[14] Monali Paul, Santosh K. Vishwakarma, Ashok Verma “Analysis of Soil Behaviour and Prediction of Crop Yield using Data Mining Approach” International Conference on Computational Intelligence and Communication Networks, IEEE-2015.
[15] Niketa Gandhi, Owaiz Petkar and Leisa J. Armstrong “Rice Crop Yield Prediction Using Artificial Neural Networks” International Conference on Technological Innovations in ICT For Agriculture and Rural Development (TIAR 2016), IEEE.
[16] Aakunuri Manjula, Dr.G .Narsimha “XCYPF: A Flexible and Extensible Framework for Agricultural Crop Yield Prediction” 9th International Conference on Intelligent Systems and Control (ISCO) IEEE-2015
[17] E.T. Papageorgioua,*, A.T. Markinosb, T.A. Gemtosb “ Fuzzy cognitive map based approach for predicting yield in cotton crop production as a basis for decision support system in precision agriculture application. Elsevier-2011, PP 3643-3657.
[18] Ashwani Kumar Kushwaha, SwetaBhattachrya “Crop yield prediction using Agro Algorithm in Hadoop” International Journal of Computer Science and Information Technology & Security (IJCSITS), Vol. 5, No2, April 2015
[19] S. Poongodi and M. Rajesh Babu “Prediction of Crop Production using Improved C4.5 with ANFIS Classifier” International Journal of Control Theory and Applications volume 10 , Number 21 , 2017.
[20] Sellam,V., Poovammal, E., “Prediction of Crop Yield using Regression Analysis”, Indian Journal of Science and Technology, Vol. 9(38), pp.1- 5, 2016.
[21] Fathima, G.N., Geetha, R., “Agriculture Crop Pattern Using Data Mining Techniques”, International Journal of Advanced Research in Computer Science and Engineering, Vol. 4, Issue 5, pp.781-786, 2014.
[22] R.Kalpana,N.Shanti and S.Arumugam ,“A survey on data mining techniques in Agriculture”,International Journal of advances in Computer Science and Technology, vol. 3, No. 8,426- 431, 2014
[23] G.N.Fatima,R.Geeta ,“Agriculture crop pattern using data mining techniques”, International Journal of Advanced Research in in Computer Science and Software Engineering, vol. 4, No. 5,781-786 , 2014 .
[24] Aditya Shastry , Sanjay H A and Madhura Hegde, “A Parameter based ANFIS Model for crop yield prediction”, IACC 2015 IEEE,pp 253-257.
[25] K. Verheyen, D. Adriaens, M. Hermy, and S. Deckers, “High resolution continuous soil classification using morphological soil profiledescriptions”, Geoderma, vol. 101, pp. 31-48, 2001.
[26] Veenadhari, S. 2007, “Crop productivity mapping based on decision tree and Bayesian classification”. Unpublished M.Tech Thesis submittedto Makhanlal Chaturvedi National University of Journalism and Communication, Bhopal.
[27] Sanjay D. Sawaitul, Prof. K.P. Wagh, Dr. P.N. Chatur, “Classification and Prediction of Future Weather by using Back Propagation Algorithm-An Approach”, International Journal of Emerging Technology and Advanced Engineering, Vol. 2, Issue 1, January 2012, pp. 110-113.
[28] V.R.Thakare and H.M. Baradkar. “Fuzzy System forMaximum Yield from Crops”, International Journal ofApplied Information Systems, pp. 4-9 , March 2013.
[29] Hemageetha, N., “A survey on application of data mining techniques to analyze the soil for agricultural purpose”, 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pp.3112-3117, 2016.
[30] Niketa Gandhi, Owaiz Petkar, Leisa J. Armstrong,” PredictingRice Crop Yield Using Bayesian Networks”, ICACCI,IEEE 2016,pp 795-799.
[31] Umid Kumar Dey, Abdullah Hasan Masud, Mohammed Nazim Uddin, “Rice Yield Prediction Model Using Data Mining”, ECCE, IEEE 2017, pp 321-326.