Abstract
Climate change affects agriculture in many ways. Reducing the vulnerability of agricultural systems to climate change and enhancing their capacity to adapt would generate better results with fewer losses. Under the conditions, according to The United Nations Food and Agriculture Organization, the world has to produce 70% more food in 2050 than it produced in 2006, to feed the growing population, it is obvious that any innovative ideas that help agriculture are optimal and needed. An option for increasing efficiency of agriculture is a data mining process that can predict climate conditions and humidity of soil. Determining the optimal time for planting and harvesting could be based on predictions from a data mining process. In this scenario, the application of collaborative data mining techniques, would offer solution for the cases in which one sources do not poses useful data for mining, and the process uses date from another sources correlated.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lika, B., Kolomvatsos, K., Hadjiefthymiades, S.: Facing the cold start problem in recommender systems. Expert Syst. Appl. 41, 2065–2073 (2014)
Moyle, S.: “Collaborative Data Mining. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA (2005)
Di Orio, G., Matei, O., Scholze, S., Stokic, D., Barata, J., Cenedese, C.: A platform to support the product servitization. IJACSA 7(2), 392–400 (2016)
Matei, O.: Preliminary results of the analysis of field data from ovens. Carpathian J. Electr. Eng. 8(1), 7–12 (2014)
Matei, O., Nagorny, K., Stoebener, K.: Applying data mining in the context of Industrial Internet. Int. J. Adv. Comput. Sci. Appl. 1(7), 621–626 (2016)
Matei, O., Di Orio, G., Jassbi, J., Barata, J., Cenedese, C.: Collaborative data mining for intelligent home appliances. In: Working Conference on Virtual Enterprises, pp. 313–323. Springer, Cham (2016)
Matei, O., Rusu, T., Petrovan, A., Mihuț, G.: A data mining system for real time soil moisture prediction. Procedia Eng. 181(837–44), 31 (2017)
Matei, O., Rusu, T., Bozga, A., Pop-Sitar, P., Anton, C.: Context-aware data mining: embedding external data sources in a machine learning process. In: International Conference on Hybrid Artificial Intelligence Systems, pp. 415–426. Springer, Cham (2017)
Matei, O., Anton, C., Scholze, S.: Multi-layered data mining architecture in the context of Internet of Things. In: IEEE 15th International Conference on Industrial Informatics (INDIN) (2017)
Camarinha-Matos, L., Afsarmanesh, H.: Collaboration forms in collaborative networks: reference modeling, pp. 51–56. Springer US (2008)
Badrul, S., et al.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web. ACM (2001)
Billsus, D., Pazzani, M.: Learning collaborative information filters. In: Proceedings of the International Conference on Machine Learning. Morgan Kaufmann Publishers. Madison (1998)
Folorunsho, O., Adeyem, A.B.: Application of data mining techniques in weather prediction and climate change studies. Int. J. Inf. Eng. Electron. Bus. 4(1), 51 (2012)
Rashid, R.A., Nohuddin, P.R., Zainol, Z.: Association rule mining using time series data for malaysia climate variability prediction. In: Badioze Zaman, H., et al. (eds.) Advances in Visual Informatics. IVIC 2017. Lecture Notes in Computer Science, vol. 10645. Springer, Cham (2017)
Niazalizadeh Moghadam, A., Ravanmehr, R.: Multi-agent distributed data mining approach for classifying meteorology data: case study on Iran’s synoptic weather stations. Int. J. Environ. Sci. Technol. 15(1), 149–158 (2018)
Namen, A., Charles, A., Rodrigues, P.: Comparison of data mining models applied to a surface meteorological station. RBRH 22, 58–67 (2017)
Comeau, D., Zhao, Z., Giannakis, D., Majda, A.: Data-driven prediction strategies for low-frequency patterns of North Pacific climate variability. Clim. Dyn. 48(5–6), 1855–1872 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Anton, C.A., Matei, O., Avram, A. (2019). Collaborative Data Mining in Agriculture for Prediction of Soil Moisture and Temperature. In: Silhavy, R. (eds) Software Engineering Methods in Intelligent Algorithms. CSOC 2019. Advances in Intelligent Systems and Computing, vol 984. Springer, Cham. https://doi.org/10.1007/978-3-030-19807-7_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-19807-7_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-19806-0
Online ISBN: 978-3-030-19807-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)