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Smart Agriculture Framework for Automated Detection of Leaf Blast Disease in Paddy Crop Using Colour Slicing and GLCM Features based Random Forest Approach

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Abstract

In the era of technological advances, the food needs of the huge population of the world could be tackled by adopting modern technologies like internet of things, artificial intelligence and cloud computing. These technologies can help in increasing the yield of food grains through precision agriculture. Paddy is one of the major food grains in the world, whose yield is mainly affected by leaf blast disease. This disease should be identified at an early stage in an automated way to mitigate its adverse impact on the yield of paddy crops through appropriate actions. In this concern, the present work proposes an automated framework for the accurate detection of blast disease affected leaves among healthy leaves of paddy plants through image processing and machine learning. This work makes use of colour slicing and grey level co-occurrence matrices (GLCM) procedures for processing and texture-related feature extraction of leaf images of paddy crop. These features are fed to machine learning classifiers viz., random forest, decision tree, kNN, XGBoost, AdaBoost and Histogram based gradient boosting algorithm. The thorough analysis of these classifiers through several performance measures reveals that random forest provides maximum accuracy of 99.10%, sensitivity of 99.05% and specificity of 99.05% for identification of leaf blast disease in paddy crops. Thus, colour slicing and GLCM features-based random forest classification approach is an appropriate choice for futuristic internet of things enabled automated framework to precisely detect blast disease affected leaves among healthy leaves of paddy crop.

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Data Availability

The datasets generated during and/or analysed during the current study are not publicly available due to confidentiality issues, but are available from the corresponding author on reasonable request.

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Acknowledgements

This work is supported by the Department of Electronics Technology, Guru Nanak Dev University, Amritsar, Punjab, India by providing exceptional computational facilities.

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The authors of this paper have not received any financial support from any institution/body/industry for this research.

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Authors and Affiliations

  1. Department of Electronics Technology, Guru Nanak Dev University, Amritsar, Punjab, India

    Amandeep Singh, Kuldeep Singh, Jaspreet Kaur & Maninder Lal Singh

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  1. Amandeep Singh
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Correspondence to Kuldeep Singh.

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Singh, A., Singh, K., Kaur, J. et al. Smart Agriculture Framework for Automated Detection of Leaf Blast Disease in Paddy Crop Using Colour Slicing and GLCM Features based Random Forest Approach. Wireless Pers Commun 131, 2445–2462 (2023). https://doi.org/10.1007/s11277-023-10545-7

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