Open Access Open Access  Restricted Access Subscription or Fee Access

Optimization of Land Suitability for Food Crops Using Neural Network and Swarm Optimization Algorithm


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/irecos.v11i1.7535

Abstract


Land quality and suitability are factors that affect food crop productivity. Inappropriate or unproductive land will impact to productivity decrease, time-consuming, and profit lost. Determining and evaluating the optimal land suitability can be done through prediction of each production area that is obtained by analyzing land fitness data. This study proposes the implementation of Neural Network and Swarm Optimization Algorithm (Particle Swarm Optimization & Cat Swarm Optimization) to obtain prediction of production. To evaluate the proposed method, this study performed comparative evaluation based on the Mean Square Error (MSE) and accuracy. Based on the experimental results, Cat Swarm Optimization produces minimum error equal of 0.00439 for training phase and 0.10453 for testing phase. In training phase, Cat Swarm Optimization produces higher accuracy (93%) than Particle Swarm Optimization (67%).
Copyright © 2016 Praise Worthy Prize - All rights reserved.

Keywords


Neural Network; Swarm Optimization Algorithm; Particle Swarm Optimization; Cat Swarm Optimization; Land Suitability

Full Text:

PDF


References


I. Brambilla and G. G. Porto, "Farm Productivity and Market Structure. Evidence From Cotton Reforms in Zambia," Economic Growth Center Discussion Paper, vol. 919, 2005.
http://dx.doi.org/10.3386/w11804

FAO, "A Framwork for Land Evaluation," FAO Soil Bulletin, vol. 32, 1976.

B. Z. Vargahan, F. Shahbazi and M. Hajrasouli, "Quantitative and Qualitative Land Suitability Evaluation For Maize Cultivation In Ghobadlou Region, Iran," Ozean Journal of Applied Sciences, vol. 4, no. 1, 2011.

N. Sevani, M. and H. Sukoco, "Sistem Pakar Penentuan Kesesuaian Lahan Berdasarkan Faktor Penghambat Terbesar (Maximum Limitation Factor) Untuk Tanaman Pangan," Jurnal Informatika, vol. 10, no. 1, pp. 23-31, 2009.
http://dx.doi.org/10.9744/informatika.10.1.23-31

A. Keshavarzi, F. Sarmadian, A. Heidari and M. Omid, "Land Suitability Evaluation Using Fuzzy Continuous Classification (A Case Study: Ziaran Region)," Modern Applied Science, vol. 4, no. 7, pp. 72-81, 2010.
http://dx.doi.org/10.5539/mas.v4n7p72

F. Susanti and S. Winiarti, "Sistem Pakar Kesesuaian Lahan Pertanian Untuk Pembudidayaan Tanaman Buah-Buahan," Jurnal Sarjana Teknik Informatika, vol. 1, no. 1, pp. 317-326, 2013.
http://dx.doi.org/10.9744/informatika.10.1.23-31

L. Zhang, J. Li, C. Kong, L. Qu, J. Zhu, Z. Chen and Y. Luo, "Using Neural Network to Evaluate Construction Land Use Suitability," Education Technology and Computer Science (ETCS), pp. 331-334, 2010.
http://dx.doi.org/10.1109/etcs.2010.446

H. and R. W. Purnawan, "Prediction of Oil Palm Production Base on Land Quality using Artificial Neural Network," Agroteknose, vol. 4, no. 2, 2009.

C. Ting, K. Wu and H. Chou, "Particle Swarm Optimization Algorithm for the Berth Allocation Problem," Expert Systems with Applications, p. 1543–1550, 2014.
http://dx.doi.org/10.1016/j.eswa.2013.08.051

P. Singh and B. Borah, "Forecasting Stock Index Price Based on M-Factors Fuzzy Time Series and Particle Swarm Optimization," International Journal of Approximate Reasoning, 2013.
http://dx.doi.org/10.1016/j.ijar.2013.09.014

N. Kayarvizhy, S. Kanmani and V. U. Rhymend, "Improving Fault Prediction using ANN-PSO in Object Oriented Systems," International Journal of Computer Applications, vol. 73, no. 3, pp. 18-25, 2013.
http://dx.doi.org/10.5120/12721-9556

J. P. Yusiong, "Optimizing Artificial Neural Networks using Cat Swarm Optimization Algorithm," Intelligent Systems and Applications, pp. 69-80, 2013.
http://dx.doi.org/10.5815/ijisa.2013.01.07

A. A. Vali, M. H. Ramesht and M. Mokarram, "The Comparison of RBF and MLP Neural Networks Performance for the Estimation of Land Suitability," Journal of Environment, vol. 2, no. 3, pp. 74-78, 2013.

H. Abdou, J. Pointon and A. El-Masry, "Neural Nets Versus Conventional Techniques in Credit Scoring in Egyptian Banking," Expert Systems with Applications, p. 1275–1292, 2008.
http://dx.doi.org/10.1016/j.eswa.2007.08.030

Sheta, A., Faris, H., Baareh, A., Predicting stock market exchange prices for the reserve bank of Australia using auto-regressive feedforward neural network model, (2015) International Review on Computers and Software (IRECOS), 10 (7), pp. 668-676.
http://dx.doi.org/10.15866/irecos.v10i7.6222

J. R. Zhang, J. Zhang, T. M. Lok and M. R. Lyu, "A Hybrid Particle Swarm Optimization Backpropagation Algorithm for Feedforward Neural Network Training," Applied Mathematics and Computation, p. 1026–1037, 2007.
http://dx.doi.org/10.1016/j.amc.2006.07.025

K. B. S. Rintyarna and A. Z. Arifin, "Klasifikasi Data Cardiotocography dengan Integrasi Metode Neural Network dan Particle Swarm Optimization," Industrial Electronics Seminar, 2011.

D. Dutta, A. Roy and K. Choudhury, "Training Artificial Neural Network using Particle Swarm Optimization Algorithm," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 3, pp. 430-434, 2013.

D. Nikelshpur and C. Tappert, "Using Particle Swarm Optimization to Pre-Train Artificial Neural Networks: Selecting Initial Training Weights for Feed-Forward Back-Propagation Neural Networks," Proceedings of Student-Faculty Research Day, CSIS, Pace University, 2013.

H. G. Nugraha and A. SN, "Optimasi Bobot Jaringan Syaraf Tiruan Mengunakan Particle Swarm Optimization," Indonesian Journal of Computing and Cybernetics Systems (IJCCS), vol. 8, no. 1, pp. 25-36, 2014.

J. Kennedy and R. C. Eberhart, "Particle Swarm Optimization," in IEEE International Conference on Neural Networks, Perth, Australia, 1995.
http://dx.doi.org/10.1109/icnn.1995.488968

A. P. Engelbrecht, Computational Intelligence: An Introduction 2nd Edition, West Sussex: John Wiley & Sons, 2007.

S. C. Chu and Y. T. Chen, "Timetable Scheduling Using Particle Swarm Optimization," First International Conference on Innovative Computing, Information and Control(ICICIC ’06), pp. 324-327, 2006.
http://dx.doi.org/10.1109/icicic.2006.541

H. D. Purnomo, Cara Mudah Belajar Metode Optimasi Metaheuristik Menggunakan Matlab, Yogyakarta: Gava Media, 2014.

Y. Shi and R. C. Eberhart, "A Modified Particle Swarm Optimizer," in Proceedings of the IEEE Conference on Evolutionary Computation, Piscataway, 1998.
http://dx.doi.org/10.1109/icec.1998.699146

R. C. Eberhart and Y. Shi, "Comparing Inertia Weights and Constriction Factors in Particle Swarm," in Proceedings of the IEEE Congress on Evolutionary Computation (CEC), San Diego, 2000.
http://dx.doi.org/10.1109/cec.2000.870279

H. J. Escalante, M. Montes and L. E. Sucar, "Particle Swarm Model Selection," Journal of Machine Learning Research, vol. 10, pp. 405-440, 2009.

S. C. Chu and P. W. Tsai, "Computational Intelligence Based on the Behavior of Cats," International Journal of Innovative Computing, Information and Control, vol. 3, no. 1, pp. 163-173, 2007.

A. Widodo, N. Naomi, Suharjito and F. Purnomo, "Prediction Of Research Topics Using Combination Of Machine Learning And Logistic Curve," Journal of Theoretical and Applied Information Technology, vol. 49, no. 3, pp. 725-732, 2013.

T. Jayalakshmi and A. Santhakumaran, "Statistical Normalization and Back Propagation," International Journal of Computer Theory and Engineering, vol. 3, no. 1, pp. 1793-8201, 2011.
http://dx.doi.org/10.7763/ijcte.2011.v3.288

J. C. Bansal, P. K. Singh, M. Saraswat, A. Verma, S. S. Jadon and A. Abraham, "Inertia Weight Strategies in Particle Swarm," in Third World Congress on Nature and Biologically Inspired Computing, 2011.
http://dx.doi.org/10.1109/nabic.2011.6089659

H. Rady, "Reyni’s Entropy and Mean Square Error for Improving the Convergence of Multilayer Backprobagation Neural Networks: A Comparative Study," International Journal of Electrical & Computer Sciences IJECS-IJENS, vol. 11, no. 5, pp. 63-74, 2011.

Paramasivam, B., Chidambaram, I.A., Design of a load-frequency controller using craziness based PSO for an interconnected power system with SSSC and RFB, (2012) International Review of Automatic Control (IREACO), 5 (2), pp. 102-112.

Ibrahim, H.E.A., Elnady, M.A., A comparative study of PID, fuzzy, fuzzy-PID, PSO-PID, PSO-fuzzy, and PSO-fuzzy-PID controllers for speed control of DC motor drive, (2013) International Review of Automatic Control (IREACO), 6 (4), pp. 393-403.

Shankar, T., Shanmugavel, S., Karthikeyan, A., Hybrid approach for energy optimization in wireless sensor networks using PSO, (2013) International Journal on Communications Antenna and Propagation (IRECAP), 3 (4), pp. 221-226.

Manh, L., Grimaccia, F., Mussetta, M., Zich, R., A Soft Computing Hybridization Technique for Antenna Optimization, (2015) International Journal on Communications Antenna and Propagation (IRECAP), 5 (1), pp. 16-20.
http://dx.doi.org/10.15866/irecap.v5i1.4899

Paulusova, J., Dubravska, M., Neuro-fuzzy predictive control, (2012) International Review of Automatic Control (IREACO), 5 (5), pp. 667-672.


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize