Document Type : Research Paper
Authors
1 Ph.D. Candidate of Agricultural Mechanization, Faculty of Agriculture, ShahidChamran University of Ahvaz, Ahvaz, Iran
2 Professor of Biosystems Engineering Department, Faculty of Agriculture, ShahidChamran University of Ahvaz, Ahvaz, Iran
3 Associate Professor of Biosystems Engineering Department, Faculty of Agriculture, ShahidChamran University of Ahvaz, Ahvaz, Iran
4 Assistant Professor of Computer Engineering Department, Faculty of Engineering, ShahidChamran University of Ahvaz, Ahvaz, Iran
Abstract
Introduction Sugarcane is a tropical, perennial grass that forms lateral shoots at the base to produce multiple stems. It is the main source of sugar production and one of the most important sources of energy production in the world. Today, the use of artificial intelligence and data mining findings to help predict product production is considered. Determining the relationship between inputs and outputs of production process using artificial intelligence (AI) has drawn more attention rather than mathematical models to find the relationships between input and output variables by training, and producing results without any prior assumptions. The adaptive neuro-fuzzy inference system (ANFIS), as a form of AI, is a combination of artificial neural network (ANN) and fuzzy systems that uses the learning capability of the ANN to derive the fuzzy if-then rules with appropriate membership functions worked out from the training pairs, which in turn leads to the inference.Particle swarm optimization (PSO) is an algorithm modeled on swarm intelligence, in a search space, or model it finds a solution to an optimization problem and predict social behavior in the presence of objectives. The PSO is a population-based stochastic computer algorithm, modeled on swarm intelligence. Swarm intelligence is based on social psychological principles and it provides insights into social behavior, also helps to many engineering applications. Feature selection is becoming very important in predictive analytics. Indeed, many data sets contain a large number of features, so we have to select the most useful ones. One of the most advanced methods to do that is the genetic algorithm (GA). Genetic algorithms can select the best subset of variables for predictive model. The purpose of this research is to evaluate the applicability of one artificial intelligence technique including adaptive neuro-fuzzy inference system and also combining this technique with particle swarm optimization to increase the accuracy and speed of training of the neuro-fuzzy system in prediction of yield and recoverable sugar percentage (R.S%) of sugarcane.
Materials and Methods In this paper, one main pattern of adaptive neuro-fuzzy inference system (ANFIS) and one synthetic model of adaptive neuro-fuzzy inference system with particle swarm optimization (PSO) were used to predict the studied properties by MATLAB version 2017. Initial data for this study were collected from Debal-Khozaie Agro-industry Company in Khouzestan province, Iran. The actual data for the seven periods of sugarcane harvest from 2010 to 2017 were used for modeling. The studied parameters included a set of agronomic factors, soil characteristics, irrigation and climate in the study area. The test data sets were used for comparison of selected ANFIS and ANFIS with PSO, as well as for the observation values. This comparison was performed by using three statistical indices: Determination Coefficient (R2), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).
Results and DiscussionFrom all of the studied parameters, eleven parameters were selected as the effective features by the binary genetic algorithm (BGA). In feature selection, the function to optimize is the generalization performance of a predictive model. More specifically, in this method, purpose was to minimize the error of the model on an independent data set not used to create the model. The data were randomly divided into two groups: training and testing. Each pattern was modeled separately and then the results were compared. The results showed that the combination of adaptive neuro-fuzzy inference system with particle swarm optimization algorithm (ANFIS-PSO) had better performance in predicting cane yield and recoverable sugar percentage. In ANFIS-PSO model the root mean square error, mean absolute percentage error and coefficient of determination values were found 0.0181, 0.0217, 0.9237 and 0.0086, 0.0138, 0.9847 respectively for two variables of cane yield and recoverable sugar percentage. In relation to the predicted cane yield by the neuro-fuzzy network with particle swarm algorithm, it can be concluded that among the effective factors, with increasing plant age and use of resistant varieties, the amount of yield was decreased and increased, respectively.
Conclusion The hybrid pattern of adaptive neuro-fuzzy inference system with the particle swarm optimization has been directed against the mere neuro-fuzzy system to a more accurate and stronger solution. Indeed, it can be concluded that ANFIS model with the PSO has the ability for precise estimation of sugarcane yield and recoverable sugar percentage.
Keywords
References
- Ahmadvand, M., Hoshmand, A.R. and Naseri, A.A. 2013. Performance forecasting of sugarcane fields using adaptive neuro-fuzzy inference system (ANFIS). Irrigation Sciences and Engineering (JISE) (Scientific Journal of Agriculture), 35(4): 1-9. (in Persian)
- Bahadori, A., Bakhshandeh, A. M. and Moradi-Telavat, M.R. 2016. Research the effect of drying-off time irrigation, time of harvest and variety factors on qualitative and quantitative yield of sugarcane. National Conference on Research and Technology Finding in Natural and Agricultural Ecosystems. Tehran. (in Persian with English abstract)
- Bagheri, S., Gheysari, M., Ayoubi, Sh. and Lavaee, N. 2012. Silage maize yield prediction using artificial neural networks. Journal of Plant Production Research, 19(4): 77-96. (in Persian with English abstract)
- Eberhart, R. and Kennedy, J. 1995. A new optimizer using particle swarm theory. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Nagoya, Japan. pp: 39-43.
- Golabi, M., Karami, B. and Albaji, M. 2013. Sensitivity analysis of sugarcane yield using artificial neural networks. 4th National Conference on Irrigation and Drainage Network Management. Faculty of Water Sciences Engineering. Shahid Chamran University of Ahvaz. pp: 1917-1924. (in Persian)
- Jang, J.S. 1993. ANFIS: adaptive-network-based on fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics, 23(3): 665-685.
- Jayashree, L.S., Rajathi, N. and Thirumal, A. 2016. Precision agriculture: On the accuracy of multilevel and clustered ANFIS models for sugarcane yield categorization. IEEE Region 10 Annual International Conference, Proceedings/TENCON, 1983–1987.
- Khashei-Siuki, A., Kouchakzadeh, M. and Ghahraman, B. 2011. Predicting dryland wheat yield from meteorological data using expert system, Khorasan Province, Iran. Journal of Agricultural Science and Technology, 13: 627-640.
- Khoshnevisan, B., Rafiee, SH., Omid, M. and Mousazadeh, H. 2014. Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs. Journal of Information Processing in Agriculture, 1: 14-22.
- Kia, S.M. 2009. Genetic Algorithms in MATLAB. Kian Publication. Tehran, 192 pages.
- Kia, S.M. 2011. Fuzzy logic in MATLAB. Kian Publication. Tehran, 304 pages.
- Lawes, R.A., McDonald, L.M., Wegener, M.K., Basford, K.E. and Lawn, R.J. 2002. Factors affecting cane yield and commercial cane sugar in the Tully district. Australian Journal of Experimental Agriculture, 42: 473-480.
- Mandla, S., Mnisi, M., Dlamini, C.S. 2011. The concept of sustainable sugarcane production: Global, African and South African perceptions. Africa Journal Agriculture Research, 7: 4337-4343.
- Menaka, K. and Yuvaraj, N. 2017. ANFIS based on crop yield prediction model. International Journal of Science, Engineering and Technology Research, 6(5): 845-854.
- Oliveria, F., Pacheco, D., Leonel, A. and Lima Neto, F. 2006. Intelligent support decision in sugarcane harvest. 4th World Congress on Computers in Agriculture. Lake Buena Vista, Florida, USA.
- Robertson, M.J. and Donaldson. R.A. 1998. Changes in the components and sucrose yield in response to drying-off of sugarcane before harvesting. Journal of Field Crop Research, 55: 201-208.
- Safavi, A.A., Pourjafarian, N. and Safavi, S.A. 2014. Optimization based on Meta-heuristic algorithms. Pejoheshgaran Nashr Daneshgahi Publications. Tehran, 224 pages.
- Sayadi Shahraki, A. Soltani Mohammadi, A., Naseri, A.A. and Mokhtaran, A. 2016. Simulation of subsurface water salinity using artificial neural network, particle swarm optimization and SEAWAT model (case study: sugarcane farms of Debal-Khozaie Agro-industry). Journal of Water and Soil Conservation. Gorgan University of Agricultural Sciences and Natural Resources, 23(5): 307-316. (in Persian with English abstract)
- Shokohfar, A. and Hajisharafi, GH. 2009. Effect of salt stress on yield and growth parameter of sugarcane. Iranian Journal of Agronomy and Plant Breeding. 5(1): 19-30. (in Persian)
- Tseng, L.Y. and Yang, S.B. 1997. Genetic algorithms for clustering feature selection and classification. IEEE Proceedings of International Conference on Neural Networks, 1612-1616.
- Tukaew, S., Datta, A., Shivakoti, G.P. and Jourdain, D. 2016. Production practices influenced yield and commercial cane sugar level of contract sugarcane farmers in Thailand. Sugar Tech, 18: 299-308.
- Zula, N.S., Sibanda, M. and Tlali, B.S. 2019. Factors affecting sugarcane production by small-scale growers in Ndwedwe Local Unicipality, South Africa. Agriculture, 9(8): 1-14.
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