Agriculture is the main source of income in India. India produces various crops such as jute, wheat, rice, sugarcane, cotton, mustard, pulses and many more. Today India acquires 2nd rank worldwide in farming production. In 16 century, 99% of world crop productions are produced by India, Now, it is only produces 23% of agriculture products. However, there are some critical factors that influence the agriculture such as in efficient use of fertilizers, Chemicals, Heavy Rainfall, Degradation/Increment in temperature, PH value, less components value present in soil. This paper provides a systematic analysis by employing data mining techniques. This paper includes some techniques of Supervised and Unsupervised learning methods. In Agriculture domain, multiple linear regressions (MLR), Support vector machine (SVM), K nearest neighbor (KNN), Density-based spatial clustering applications with noise (DBSCAN) are the most widely used data mining algorithm which aimed to solve the issues of the agriculture up to some extent.

Agriculture, Yield prediction, Data Mining, MLR, KNN, DBSCAN, SVM, Clustering and Classification

[1] D Ramesh, B Vishnu Vardhan, “Analysis of Crop Yield Prediction Using Data Mining Techniques”, International Journal of Advanced Research in Engineering and Technology.
[2] Nikita Gandhi, Leisa Armstrong, “Applying Data Mining Techniques to Predict Yield of Rice in Humid Subtropical Climatic Zone of India”.
[3] Monali Paul, Santosh K. Vishwakarma and Ashok Verma, “Analysis of Soil Behaviour and Prediction of Crop Yield using Data Mining Approach”, IEEE.
[4] Dr. S. Hari Ganesh, Mrs. Jayasudha, “Data Mining Technique to Predict Accuracy of the Soil Fertility”,International Journal of Computer Science and Mobile Computing.
[5] Nikita Gandhi, Leisa Armstrong, “Rice Crop Yield Forecasting of Tropical Wet and Dry Climatic Zone of India Using Data Mining Techniques", 2016 3rd International Conference, Computing for Sustainable Global Development, Pg: 357-363.
[6] Anitha, “A Predictive Modeling Approach for Improving Paddy Crop Productivity using Data Mining Techniques”, Turkish Journal of Electrical Engineering and Computer Science 2017, Pg: 4777-4787.
[7] G.Nasrin Fathima, R.Geetha, “Agriculture Crop Pattern Using Data Mining Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering, Issue 5,May 2014, Pg: 781-786
[8] Vinayak A. Bhardi, Prachi P. Abhyankar, Ravina S.Patil, Sonal S. Patade, Tejaswani U.Nate, Anaya M.Joshi, “Analysis and Prediction in Agricultural Data using Data Mining Techniques” IJRISE.
[9] Jharna Majumdar, Sneha Naraseeyappa and Shilpa Ankalaki, “Analysis of Agriculture Data using Data Mining Techniques”, Journal of Big data, Springer Open 2017, Pg: 3-15.
[10] Sally Jo Cunningham, Geoffrey Holmes, “Developing Innovative in Agriculture Using Data Mining Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering.
[11] Umid Kumar Dey, Abdullah Hasan Masud, Mohammed Nazim Uddin, “Rice Yield Prediction Model Using Data Mining Techniques”, International Conference on Electrical, Computer and Communication Engineering, February 16-18, 2017, Pg: 321-326.
[12] S. Pudumalar, E.Ramanujam, R. Harine Rajashree, C. Kavya, T. Kiruthika, J. Nisha, “Crop Recommendation System for Precision Agriculture”, 2016 IEEE EIGHT International Conference on Advanced Computing, Pg: 32-36. [13] Neetu Chahal, Anuradha, “A Study on Agricultural Image Processing along with Classification Model”, 2015 IEEE International Advance Computing Conference, , Pg: 942-947.
[14] Pallavi V. Jirapure, Prarthana A. Deshkar, “Qualitative Data Analysis Using Regression Method for Agricultural Data”, 2016 IEEE.
[15] Nikhil Sethi, Dr. Kanwal Garg, “Exploiting Data Mining Technique for Rainfall Prediction”, International Journal of Computer Science and Information Technologies, Vol. 5(3), 2014, Pg: 3982-3984.
[16] Pooja G. Mate, Kavita R. Singh, Anand Khobragade, “Feature Extraction Algorithm for Estimation of Agriculture Acreage from Remote Sensing Images”, 2016 IEEE, Pg: 1-5.
[17] Chowdari K.K, Dr. Girisha R, Dr. K C Gouda, “A Study of Rainfall Over India Using Data Mining”, 2015 IEEE Sponsored International Conference on Emerging Research in Electronics, Computer Science and Technology, Pg: 44-47.
[18] Kuljit Kaur, Kanwakpreet Singh Atwal, “Effect of Temperature and Rainfall on Paddy Yield using Data Mining”, 2017 Seventh International Conference on Cloud Computing, Data Science and Engineering, Pg: 508-511.
[19] Raorane A. A., Kulkarni R. V., “Data Mining: An Effective Tool for Yield Estimation in the Agriculture Sector”, International Journal of Emerging Trends and Technology in Computer Science, July- August 2012, pg.75-79.
[20] P. Hariharan, K.Arulanandham, “Design an Disease Predication Application Using Data Mining Techniques for Effective Query Processing Results”, Advances in Computational Sciences and Technology.
[21] N. Gandhi ,L.Armstrong, O.Petkar and A. Tripathy, “Rice Crop Yield Prediction in India Using Support Vector Machine”, In IEEE, 2016.
[22] T. Ranjeet and L. Armstrong, “An Artificial Neural Network for Predicting Crop Yield in Nepal”, Ninth Conference for Information Technology in Agriculture” ICT’s for future Economic and Sustainable Agricultural Systems”, Perth, Australia.
[23] S. Jabjone and S. Wannasang, “Decision Support System Using Aritifical Neural Network to Predict Rice Production in Phimai Thailand”, IJSCM.
[24] Mehmed Kantardzic, “Data-Mining Concepts”, Edition:1, Copyright year: 2011.
[25] Mrs. Bharati, M. Ramageri “Data Mining Techniques and Applications”, IJCSE
[26] Fuzail Misarwala, KausarMukadam, and Kiran Bhowmick, “Applications of Data Mining in Fraud Detection”, IJCSE.