NSGRF: Plant Leaf Disease Detection Using Multi Objective Genetic Algorithm Features Optimization with Random Forest
NSGRF: Plant Leaf Disease Detection Using Multi Objective Genetic Algorithm Features Optimization with Random Forest |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-3 |
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| Year of Publication : 2025 | ||
| Author : Harminder Kaur, Neeraj Raheja |
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| DOI : 10.14445/22315381/IJETT-V73I3P134 | ||
How to Cite?
Harminder Kaur, Neeraj Raheja, "NSGRF: Plant Leaf Disease Detection Using Multi Objective Genetic Algorithm Features Optimization with Random Forest," International Journal of Engineering Trends and Technology, vol. 73, no. 3, pp. 487-502, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I3P134
Abstract
The agricultural sector in India, a developing nation, relies heavily on agriculture for its population’s livelihood and food security. Plants are essential for sustenance, medicine, and various industries. India’s agricultural sector contributes 17% to the GDP and ranks among the top three nations in producing staple crops like rice and wheat and cash crops like cotton and vegetables. This study is focused on the production and subsequent evaluation of an advanced plant disease classification system using the Plant Village dataset. The proposed approach integrates texture feature extraction, multi-objective optimization, and Random Forest Classifier to enhance classification accuracy and efficiency. Texture features are extracted from the Gray-Level Co-Occurrence Matrix (GLCM) and using Local Binary Patterns (LBP), providing a low-dimensional yet interpretable representation of leaf patterns. This feature extraction method reduces overfitting, especially in scenarios with limited sample sizes. The Random Forest parameters are optimized using multi-objective evolutionary algorithms like NSGA-II, ensuring a balance between model complexity and generalization capability. A genetic algorithm is employed for feature selection and weighting, which, combined with Random Forest techniques, further refines the classification performance. Extensive testing demonstrates the suggested method’s effectiveness, significantly outperforming previous methods. The NSGRF approach, a key element of the system, significantly outperforms in terms of precision, accuracy, recall, and F-score. The study’s findings indicate that this integrated approach is more accurate and offers faster inference times and improved interpretability; in this way, it can be a significant device for automatically detecting plant diseases in the agricultural technology field. In the experiment survey, the algorithm NSGRF improves 2% accuracy, 1 % precision, 2 % recall, and 1% f-score compared to other approaches.
Keywords
Plant disease, Feature extraction, Multi-objective optimization, Machine Learning, Genetic Algorithm.
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