ICTACT Journal on Soft Computing

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Optimizing Plant Disease Prediction: A Neuro-fuzzy-genetic Algorithm Approach


Affiliations
1 Department of Electronics and Computer Engineering, Sanjivani College of Engineering, India
2 Department of Computer Science and Engineering, Amrita College of Engineering and Technology, India
3 Department of Electronics and Communication Engineering, VSB College of Engineering Technical Campus, India
4 Department of Mathematics, Vishwavidyalaya Engineering College, India
5 Micron Technology, United States
     

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In this essay, the idea of improving plant disease prediction using a Neuro-Fuzzy-Genetic algorithm (NFGA) technique is explored. The concept of a Neuro-Fuzzy-Genetic algorithm is first described. The advantages of the NFGA technique for predicting plant disorders are next addressed. An example is then given to demonstrate how this technique has been successfully used and the advantages it offers you. A hybrid artificial intelligence technique known as a Neuro-Fuzzy-Genetic set of rules (NFGA) combines the genetic algorithms, fuzzy logic, and neural network algorithms. It entails using a method of organizing fuzzy rules for statistics type, developing a network of neurons to anticipate the level of the group of data points to positive fuzzy training, adjusting the weights of fuzzy classes using a genetic algorithm-based totally optimization method to better fit the data factors, and ultimately identifying and predicting patterns of statistics points. The main benefits of this method for predicting plant diseases are its abilities to analyze various plant characteristics, extract complex relationships between statistics points, identify correlations between various environmental factors and illnesses, choose the best combinations of fuzzy rules for accurate classification, and finally adapt to changing data over time

Keywords

Plant Disease Prediction, Neuro-Fuzzy-Genetic Algorithm, Optimization, Machine Learning, Classification, Feature Extraction.
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  • Optimizing Plant Disease Prediction: A Neuro-fuzzy-genetic Algorithm Approach

Abstract Views: 127  |  PDF Views: 1

Authors

Sachin Vasant Chaudhari
Department of Electronics and Computer Engineering, Sanjivani College of Engineering, India
T. S. Sasikala
Department of Computer Science and Engineering, Amrita College of Engineering and Technology, India
R. K. Gnanamurthy
Department of Electronics and Communication Engineering, VSB College of Engineering Technical Campus, India
Vijay Kumar Dwivedi
Department of Mathematics, Vishwavidyalaya Engineering College, India
Davinder Kumar
Micron Technology, United States

Abstract


In this essay, the idea of improving plant disease prediction using a Neuro-Fuzzy-Genetic algorithm (NFGA) technique is explored. The concept of a Neuro-Fuzzy-Genetic algorithm is first described. The advantages of the NFGA technique for predicting plant disorders are next addressed. An example is then given to demonstrate how this technique has been successfully used and the advantages it offers you. A hybrid artificial intelligence technique known as a Neuro-Fuzzy-Genetic set of rules (NFGA) combines the genetic algorithms, fuzzy logic, and neural network algorithms. It entails using a method of organizing fuzzy rules for statistics type, developing a network of neurons to anticipate the level of the group of data points to positive fuzzy training, adjusting the weights of fuzzy classes using a genetic algorithm-based totally optimization method to better fit the data factors, and ultimately identifying and predicting patterns of statistics points. The main benefits of this method for predicting plant diseases are its abilities to analyze various plant characteristics, extract complex relationships between statistics points, identify correlations between various environmental factors and illnesses, choose the best combinations of fuzzy rules for accurate classification, and finally adapt to changing data over time

Keywords


Plant Disease Prediction, Neuro-Fuzzy-Genetic Algorithm, Optimization, Machine Learning, Classification, Feature Extraction.

References