Evolutionary Learning of Fuzzy Rules and Application to Forecasting Environmental Impact on Plant Growth
by Chris Nikolopoulos 1,* 
and Ryan Koralik1
1 Department of Computer Science, Bradley University, Peoria, IL 61625, USA
2 Data Science Manager, Nielsen, Schaumburg, IL 60173, USA
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 1, Issue 4, Page # 48-53, 2022; DOI: 10.55708/js0104006
Keywords: Fuzzy logic, Genetic algorithms, Machine learning, Forecasting in Agriculture
Received: 21 February 2022, Revised: 26 March 2022, Accepted: 27 March 2022, Published Online: 12 April 2022
APA Style
Nikolopoulos, C., & Koralik, R. (2022, April). Evolutionary Learning of Fuzzy Rules and Application to Forecasting Environmental Impact on Plant Growth. Journal of Engineering Research and Sciences, 1(4), 48–53. https://doi.org/10.55708/js0104006
Chicago/Turabian Style
Nikolopoulos, Chris, and Ryan Koralik. “Evolutionary Learning of Fuzzy Rules and Application to Forecasting Environmental Impact on Plant Growth.” Journal of Engineering Research and Sciences 1, no. 4 (April 2022): 48–53. https://doi.org/10.55708/js0104006.
IEEE Style
C. Nikolopoulos and R. Koralik, “Evolutionary Learning of Fuzzy Rules and Application to Forecasting Environmental Impact on Plant Growth,” Journal of Engineering Research and Sciences, vol. 1, no. 4, pp. 48–53, Apr. 2022, doi: 10.55708/js0104006.
Prediction of plant growth and yield is one of the essential tasks that enables growers of food and agricultural products to effectively manage their crops. In this paper, a hybrid evolutionary/fuzzy machine learning approach is introduced where a genetic algorithm is deployed to learn the optimum membership functions of relevant fuzzy sets and a knowledge base of fuzzy rules. This hybrid approach is then used to build a model which determines how ozone and carbon dioxide levels in the atmosphere affect plant growth by predicting the basal width growth of a plant. The hybrid forecasting model was tested on a data set collected from soybean fields and proved to be an extremely accurate and robust fuzzy predictor. It was able to predict the basal width growth of the plant with an average of 0.19% relative absolute value error.
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