RECENT DEVELOPMENTS IN AGRICULTURE USING DATA MINING TECHNIQUES

Authors

  • V.Manimekalai Assistant Professor, Department of Computer Technology, Dr.N.G.P Arts & Science College, Coimbatore 641048.
  • R.Suresh Assistant Professor, Department of Computer Technology, Dr.N.G.P Arts & Science College, Coimbatore 641048.

Keywords:

Clustering, Fermentations, Cross-validation, k-means algorithm, Bi-cluster

Abstract

This survey covers some very recent applications of data mining techniques in the field of agriculture. This is an emerging research field that is experiencing a constant development. In this paper, we first present two applications in this field in details; in particular, we consider the problem of discovering problematic wine augmentations at the early stages of the process, and the problem of predicting yield production by using sensor data information. Secondly, we briefly describe other problems in the field for which we found very recent contributions in the Scientific literature.

References

S. Arivazhagan, R.N. Shebiah, S.S. Nidhyanandhan, L. Ganesan, Fruit Recognition using Color and Texture Features, Journal of Emerging Trends in Computing and Information Sciences 1(2), 90–94, 2010.

P. Baranowski, W. Mazurek, Detection of Physiological Disorders and Mechanical Defects in Apples using Thermography, International Agrophysics 23, 9–17, 2009.

S. Busygin, O.A. Prokopyev, P.M. Pardalos, Feature Selection for Consistent Bi-clustering via Fractional 0-1 Programming, Journal of Combinatorial Optimization 10, 7-21, 2005.

P. Cortez, A. Cerdeira, F. Almeida, T. Matos, J. Reis, Modeling Wine Preferences by Data Mining from Physicochemical Properties, Decision Support Systems 47(4), 547–553, 2009.

S. Cubero, N. Aleixos, E. Molt´o, J. G´omez-Sanchis, J. Blasco, Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables, Food and Bioprocess Technology 4(4), 487–504, 2011.

L. Ding, J. Meng, Z. Yang, An Early Warning System of Pork Price in China Based on Decision Tree, IEEE Conference Proceedings, International Conference on E-Product E-Service and E-Entertainment (ICEEE), Henan, China, 1–6, 2010.

D.A. Griffith, Spatial Autocorrelation and Spatial Filtering, Advances in Spatial Science Series, Springer, New York, 2003.

M. Guarino, P. Jans, A. Costa, J-M. Aerts, D. Berckmans, Field Test of Algorithm for Automatic Cough Detection in Pig Houses, Computers and Electronics in Agriculture 62(1), 22–28, 2008.

D.S. Guru, Y.H. Sharath, S. Manjunath, Texture Features and KNN in Classification of Flower Images, International Journal of Computer Applications 1, Special Issue “Recent Trends in Image Processing and Pattern Recognition”, 21–29, 2010.

R.P. Haff, Real-Time Correction of Distortion in X-ray Images of Cylindrical or Spherical Objects and its Application to Agricultural Commodities, Transactions of the American Society of Agricultural and Biological Engineers 51(1), 341–349, 2007.

R.P. Haff, N. Toyofuku, X-ray Detection of Defects and Contaminants in the Food Industry, Sensing and Instrumentation for Food Quality and Safety 2(4), 262–273, 2008.

J. Hartigan, Clustering Algorithms, John Wiles & Sons, New York, 1975. 13. M. Kovacevic, B. Bajat, B. Gajic, Soil Type Classification and Estimation of Soil Properties using Support Vector Machines, Geoderma 154(3–4), 340–347, 2010.

O.E. Kundakcioglu, P.M. Pardalos, The Complexity of Feature Selection for Consistent Biclustering, In: Clustering Challenges in Biological Networks, S. Butenko, P.M. Pardalos, W.A. Chaovalitwongse (Eds.), World Scientific Publishing, 2009.

A. Mucherino, A. Urtubia, Consistent Biclustering and Applications to Agriculture, IbaI Conference Proceedings, Proceedings of the Industrial Conference on Data Mining (ICDM10), Workshop “Data Mining in Agriculture” (DMA10), Berlin, Germany, 105-113, 2010.

Additional Files

Published

15-06-2017

How to Cite

V.Manimekalai, & R.Suresh. (2017). RECENT DEVELOPMENTS IN AGRICULTURE USING DATA MINING TECHNIQUES. International Education and Research Journal (IERJ), 3(6). Retrieved from http://ierj.in/journal/index.php/ierj/article/view/1077