Naheed Ejaz, Shabbir Abbasi


The production of wheat plays an important role in Pakistan’s economy. Wheat yield forecasting is significant farming problem as it’s the most important crop of Pakistan. Prediction of the wheat yield has been determined by data mining techniques with different environmental factors. Data mining techniques have been developed for analysing and implementation on wheat yield dataset to predict the yield which is very helpful to produce wheat. In this study, Neural Network and a Novel Integrated approach of Neural Network, Support Vector Machine and Regression are used to analyze and estimates the wheat yield production. We have used these predictive techniques with area, yield, production, soil pH, temperature, air pressure, rainfall, water availability, humidity, pesticides and fertilizer parameter for wheat yield prediction.

Full Text:



Kim, S., & B. E. Dale, 2004. Global potential bioethanol production from wasted crops and crop residues.

Biomass and bioenergy, 26(4):361-375.

Dixon, R. A., and G. M. Pasinetti, 2010. Flavonoids and isoflavonoids: from plant biolog to agricul-ture

andneuroscience. Plant Physiology.154:453-457

P. Shewry, 2009. Journal of experimental botany. 60:1537-1553.

Akhtar, L., M. Hussain, A. Tariq, and M. Nasim, 2010. A review of hundred years of wheat research and

development in Punjab. Pak J Sci. 40:128-34.

Okong’o, N., A. Leboissetier, and J. Bellan, 2008. Detailed characteristics of drop-laden mixing lay-ers: Large eddy

simulation predictions compared to direct numerical simulation. Physics of Flu-ids, 20(10):103305.

Ramesh, D., & B. V. Vardhan, 2013. Data mining techniques and applications to agricultural yield data. International

Journal of Advanced Research in Computer and Communication Engineer-ing, 2(9): 3477-80.

Han, J., J. Pei, and M. Kamber, 2011. Data mining: concepts and techniques. Elsevier.

Supit, I., 1994. System description of the wofost 6.0 crop simulation model implemented in cgms. Theory and

algorithms, 1: 146.

Stöckle, C.O., M. Donatelli and R. Nelson, 2003. Cropsyst, a cropping systems simulation model. European journal

of agronomy, 18(3-4): 289-307.

Feelders, A., H. Daniels, and M. Holsheimer, 2000. Methodological and practical aspects of data mining. Information

& Management, 37(5):271-281.

Zhang, Y. and Y. He, 2005. Study of prediction model on grey relational bp neural network based on rough set. In

Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on, (4764-4769).

He, Y., Y. Zhang, S. Zhang and H. Fang, 2006. Application of artificial neural network on relationship analysis

between wheat yield and soil nutrients. Engineering in Medicine and Biology Society, 27th Annual International

Conference of the, IEEE

Crone, S.F., S. Lessmann and S. Pietsch, 2006. Forecasting with computational intelligence-an evaluation of support

vector regression and artificial neural networks for time series prediction. In Neural Networks, IJCNN'06.

International Joint Conference on (3159-3166).

Ruß, G., R. Kruse, M. Schneider, and P. Wagner, 2008. Data mining with neural networks for wheat yield prediction.

Advances in Data Mining. Medical Applications, E-Commerce, Marketing, and Theoretical Aspects, 47-56.

Alvarez, R. 2009. Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial

neural network approach. European Journal of Agronomy 30(2): 70-77.

Esfandiary, F., G. Aghaie and A.D. Mehr, 2009. Wheat yield prediction through agro meteorological indices for

ardebil district. World Academy of Science, Engineering and Technology, 49: 32-35.

Marinkovic, B., J. Crnobarac, S. Brdar, B. Antic, G. Jacimovic and V. Crnojevic, 2009. Data mining approach for

predictive modeling of agricultural yield data. Proc. First Int Workshop on Sensing Technologies in Agriculture,

Forestry and Environment Novi Sad, Serbia.

Cheng, F., F. Chen and Y. Ying, 2010. Image recognition of unsound wheat using artificial neural network. In

Intelligent Systems (GCIS), Second WRI Global Congress on (172-175).

Wang, H. and Z. Ma, 2011. Prediction of wheat stripe rust based on support vector machine. In Natural Computation

(ICNC), Seventh International Conference on (378-382).

Du, X., J. Meng, I. Savin and Q. Li, 2013. Wheat yield estimation in russia with modis time-series data based on light

use efficiency model. In Geoscience and Remote Sensing Symposium (IGARSS), IEEE International, 2848-2851.

Romero, J.R., P.F. Roncallo, P.C. Akkiraju, I. Ponzoni, V.C. Echenique and J.A. Carballido, 2013. Using

classification algorithms for predicting durum wheat yield in the province of buenos aires. Computers and electronics

in agriculture, 96: 173-179.

Çakır, Y., M. Kırcı and E.O. Güneş, 2014. Yield prediction of wheat in south-east region of turkey by using artificial

neural networks. In Agro-geoinformatics (Agro-geoinformatics ), Third International Conference on (1-4).

Kadir MK, Ayob MZ, Miniappan N. Wheat yield prediction: Artificial neural network based ap-proach.

InEngineering Technology and Technopreneuship (ICE2T), 2014 4th International Con-ference on 2014 Aug 27 (pp.


Guo, W. and H. Xue, 2014. Crop yield forecasting using artificial neural networks: a com-parison between spatial

and temporal models. Mathematical Problems in Engineering.

Janjua, P.Z., G. Samad and N. Khan, 2014. Climate change and wheat production in pakistan: An autoregressive

distributed lag approach. NJAS-Wageningen Journal of Life Sciences, 68: 13-19.

Ahamed, A.M.S., N.T. Mahmood, N. Hossain, M.T. Kabir, K. Das, F. Rahman and R.M. Rahman, 2015. Applying

data mining techniques to predict annual yield of major crops and recommend planting different crops in different

districts in bangladesh. In Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed

Computing (SNPD), 16th IEEE/ACIS International Conference on ( 1-6).

Geetha, M., 2015. A survey on data mining techniques in agriculture. International journal of inno-vative research in

computer and communication engineering, 3(2): 887-892.

Kumar, R., M. Singh, P. Kumar and J. Singh, 2015. Crop selection method to maximize crop yield rate using

machine learning technique. In Smart Technologies and Management for Computing, Communication, Controls,

Energy and Materials (ICSTM), International Conference on (138-145).

Osman, J., J. Inglada and J.-F. Dejoux. 2015. Assessment of a Markov logic model of crop rotations for early crop

mapping. Computers and Electronics in Agriculture. 113:234- 243.

Shakoor, M.T., K. Rahman, S.N. Rayta and A. Chakrabarty, 2017. Agricultural production output prediction using

supervised machine learning techniques. In Next Generation Computing Appli-cations (NextComp), 1st

International Conference on (182-187).

Krikke, J. &Alfonsi, B., 2006. In the News. IEEE Intelligent Systems, 21(3):102–104, c3.

Ye, Nong, 2013; Data Mining: Theories, Algorithms, and Examples, CRC Press.

Dey, U.K., A.H. Masud and M.N. Uddin, 2017. Rice yield prediction model using data mining. In Electrical,

Computer and Communication Engineering (ECCE), International Conference on (321-326.

DOI: http://dx.doi.org/10.22555/pjets.v8i2.2231


Chief Editor

Prof. Dr. Tariq Rahim Soomro 
College of Computer Science & Information Systems

Editorial Advisory Board (Internal)

Dr. Syed Irfan Hyder
Dr. S.M. Aqil Burney
Dr. Ejaz Ahmed
Dr. Mohammad Irshad Khan
Dr. Shahid Amjad
Dr. Fatima Riaz
Dr. Insia Hussain
Dr. Ehsan Rehman
Dr. Imran Majid
Dr. Khurram Iqbal
Dr. Zeeshan Shahid
Dr. Seema Ansari
Dr. Muhammad Mansoor Alam


Editorial Advisory Board (International)

Prof. Dr. Mazliham Mohd Su'ud, President, Multimedia University, Malaysia

Dr. Eiad Yafi, Deputy Dean, Institute of Post Graduate Studies, Universiti Kuala Lumpur, Malaysia

Prof. Dr. Ghassan Al-Qaimari, President, Emirates College of Technology, Abu Dhabi, UAE
Prof. Dr. Patrice Boursier, Universite de La Rochelle, La Rochelle, France
Prof. Dr. Mudassir Uddin, Professor, University of Karachi, Pakistan
Dr. Nadeem Doudpota, Associate Professor, Al-Baha University, KSA
Dr. Haithem Abdelrazaq Almefleh, Associate Professor, Yarmouk University, Yarmouk, Jordan
Dr. Saiful Islam Ansari, Assistant Professor, University of Tabuk, Saudi Arabia