Performance Evaluation of Artificial Intelligence on Soil Property Detection

Authors

  • Smriti Singhatiya P.G. Student, Department of Computer Science aP.G. StudentDepartment of Computer Science and Engineering Maharana Pratap College of Technology Gwalior,Indiand Engineering, MPCT, Gwalior, India
  • Dr. Shivnath Ghosh Associate Professor Department of Computer Science and Engineering Maharana Pratap College of Technology Gwalior,India

DOI:

https://doi.org/10.24113/ijoscience.v4i10.166

Keywords:

Yield Prediction, Data Mining, Soil Analysis,SVR, NN, ER, MSE, RMSE

Abstract

Now-a-days there is a need to study the nutrient status in lower horizons of the soil. Soil testing has played historical role in evaluating soil fertility maintenance and in sustainable agriculture. Soil testing shall also play its crucial role in precision agriculture. At present there is a need to develop basic inventory as per soil test basis and necessary information has to be built into the system for translating the results of soil test to achieve the crop production goal in new era. To achieve this goal artificial intelligence approach is used for predicting the soil properties.  In this paper for analysing these properties support vector regression (SVR), ensembled regression (ER) and neural network (NN) are used. The performance is evaluated with respect to MSE and RMSE and it is observed that ER outperforms better with respect to SVR and NN.

Downloads

Download data is not yet available.

References

Mucherino, P. Papajorgji, P.M. Pardalos, “Data Mining in Agriculture”, Springer, 2009.

Mucherino, Petraq Papajorgji, P. M. Pardalos,” A survey of data mining techniques applied to agriculture”, 25 May 2009 Springer-Verlag 2009.

Sally Jo Cunningham and Geoffrey Holmes,”Developing innovative applications in agriculture using data mining“, Department of Computer Science, University of Waikato Hamilton, New Zealand.

Cover TM, Hart PE,” K Nearest Neighbor pattern classification”, IEEE Trans Info Theory 13(1): 21-27, 1967.

P.Bhargavi, Dr.S.Jyothi, “Applying Naive Bayes Data Mining Technique for Classification of Agricultural Land Soils”, IJCSNS International Journal of Computer Science and Network Security, Vol.9 No.8, August 2009.

Dr. Shivnath Ghosh, S. S. (2018). A Review on Soil Property Detection using Machine Learning Approach. SMART MOVES JOURNAL IJOSCIENCE, 4(8), 6. https://doi.org/10.24113/ijoscience.v4i8.152.

Georg Rub, Rudolf Kruse, Martin Schneider and Peter Wagner, “Data Mining with Neural Networks for Wheat Yield Prediction”.

Shweta Taneja, Rashmi Arora, Savneet Kaur, “Mining of Soil Data Using Unsupervised Learning Technique”, International Journal of Applied Engineering Research, ISSN 0973-4562 Vol. 7 No.11, 2012.

M. C. S. Geetha,” Implementation of Association Rule Mining for different soil types in Agriculture”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 4, Issue 4, April 2015.

M. Soundarya, R. Balakrishnan,” Survey on Classification Techniques in Data mining”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 7, July 2014.

D Ramesh, B Vishnu Vardhan, “Data mining Techniqued and applications to agriculture yield data”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 2, Issue 9, September 2013.

Gideon O Adeoye, Akinola A Agboola, “Critical levels for soil pH, available P, K, Zn and Mn and maize ear-leaf content of P, Cu and Mn in sedimentary soils of South Western Nigeria”, Nutrient Cycling in Agroecosystems, Volume 6, Issue 1, pp 65-71, February 1985.

D. Almaliotis, D. Velemis, S. Bladenopoulou, N. Karapetsas, “Appricot yield in relation to leaf nutrient levels in Northern Greece”, ISHS Acta Horticulturae 701: XII International Symposium on Apricot Culture and Decline.

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, 2015.

Downloads

Published

10/13/2018

How to Cite

Singhatiya, S., & Ghosh, D. S. (2018). Performance Evaluation of Artificial Intelligence on Soil Property Detection. SMART MOVES JOURNAL IJOSCIENCE, 4(10), 18–21. https://doi.org/10.24113/ijoscience.v4i10.166