Utilization of artificial immune system in prediction of paddy production

This paper proposed an Artificial Immune System (AIS) approach using the Clonal Selection Based Algorithms (CSA) to analyze the pattern recognition capability of the paddy trend, and to predict the paddy production based on climate change effects. Climate factors and paddy production are used as input parameters. High percentage of accuracy ranges from 90%-92% is obtained throughout the training, validation and testing steps of the model. The results of the study were tested using the Root Mean Square Error (RMSE), Mean Average Percentage Error (MAPE) and coefficient of determination (R2). Based on the results of this study, it can be concluded that the CSA is a reliable tool to be used as pattern recognition and prediction of paddy production.

[1]  Liangpei Zhang,et al.  A Supervised Artificial Immune Classifier for Remote-Sensing Imagery , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Jonathan Timmis,et al.  Theoretical advances in artificial immune systems , 2008, Theor. Comput. Sci..

[3]  Wendy S. Merritt,et al.  CATCHCROP: modeling crop yield and water demand for integrated catchment assessment in Northern Thailand , 2002, Environ. Model. Softw..

[4]  N. Subash,et al.  Evaluation of the impact of climatic trends and variability in rice–wheat system productivity using Cropping System Model DSSAT over the Indo-Gangetic Plains of India , 2012 .

[5]  Kau,et al.  Impact of climate change on rice production , 2015 .

[6]  Satyasai Jagannath Nanda,et al.  Artificial Immune Systems: Principle, Algorithms and Applications , 2009 .

[7]  Salah Al-Sharhan,et al.  ARTIFICIAL IMMUNE SYSTEMS - MODELS, ALGORITHMS AND APPLICATIONS , 2010 .

[8]  Mohamed Y. Hashim,et al.  Evaluation in the Malaysian Agricultural Research and Development Institute , 1987 .

[9]  Bo Chen,et al.  Artificial immune pattern recognition for structure damage classification , 2009 .

[10]  P. Karn.  The Impact of Climate Change on Rice Production in Nepal , 2014 .

[11]  K. Lakshmi,et al.  Gencos wind–thermal scheduling problem using Artificial Immune System algorithm , 2014 .

[12]  Jagadish Timsina,et al.  Performance of CERES-Rice and CERES-Wheat models in rice–wheat systems: A review , 2006 .

[13]  Jonathan Timmis,et al.  Artificial Immune Systems: A New Computational Intelligence Approach , 2003 .

[14]  Yongsheng Ding,et al.  An ensemble kernel classifier with immune clonal selection algorithm for automatic discriminant of primary open-angle glaucoma , 2012, Neurocomputing.

[15]  Karl J. Friston,et al.  Predicting IQ change from brain structure: A cross-validation study , 2013, Developmental Cognitive Neuroscience.