Feature Selection Using Quantum Inspired Island Model Genetic Algorithm for Wheat Rust Disease Detection and Severity Estimation

Sourav Samanta, Sanjay Chatterji, Sanjoy Pratihar

2024

Abstract

In the context of smart agriculture, an early disease detection system is crucial to increase agricultural yield. A disease detection system based on machine learning can be an excellent tool in this regard. Wheat is one of the world’s most important crops. Leaf rust is one of the most significant wheat diseases. In this work, we have proposed a method to detect the leaf rust disease-affected areas in wheat leaves to estimate the severity of the disease. The method works on a reduced Color-GLCM (C-GLCM) feature set. The proposed feature selection method employs Quantum Inspired Island Model Genetic Algorithm to select the most compelling features from the C-GLCM set. The proposed feature selection method outperforms the classical feature selection methods. The healthy and diseased leaves are classified using four classifiers: Decision Tree, KNN, Support Vector Machine, and MLP. The MLP classifier achieved the highest accuracy of 99 .20% with the proposed feature selection method. Following the detection of the diseased leaf, the k-means algorithm has been utilized to localize the lesion area. Finally, disease severity scores have been calculated and reported for various sample leaves.

Paper Citation

in Harvard Style

Samanta S., Chatterji S. and Pratihar S. (2024). Feature Selection Using Quantum Inspired Island Model Genetic Algorithm for Wheat Rust Disease Detection and Severity Estimation. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 492-499. DOI: 10.5220/0012380000003660

in Bibtex Style

@conference{visapp24,
author={Sourav Samanta and Sanjay Chatterji and Sanjoy Pratihar},
title={Feature Selection Using Quantum Inspired Island Model Genetic Algorithm for Wheat Rust Disease Detection and Severity Estimation},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={492-499},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012380000003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Feature Selection Using Quantum Inspired Island Model Genetic Algorithm for Wheat Rust Disease Detection and Severity Estimation
SN - 978-989-758-679-8
AU - Samanta S.
AU - Chatterji S.
AU - Pratihar S.
PY - 2024
SP - 492
EP - 499
DO - 10.5220/0012380000003660
PB - SciTePress