Authors:
Sourav Samanta
;
Sanjay Chatterji
and
Sanjoy Pratihar
Affiliation:
Department of Computer Science & Engineering, Indian Institute of Information Technology, Kalyani, West Bengal, India
Keyword(s):
Smart Agriculture, Wheat Leaf Rust Disease Detection, Quantum Inspired Island Model Genetic Algorithm, Color-Glcm Features, Disease Severity Score.
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. Fol
lowing 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.
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