Optimization-Based Auto-Metric Graph Neural Network Framework for Rice Leaf Disease Classification

Authors

  • Veeramreddy Rajasekhar Research Scholar, Department of Computer Science and Engineering Faculty of Engineering and Technology, Annamalai University, Chidambaram, Tamil Nadu, India.
  • Gnanasekaran Arulselvi Associate Professor, Department of Computer Science and Engineering Faculty of Engineering and Technology, Annama*lai University, Chidambaram, Tamil Nadu, India.
  • K. Suresh Babu Professor & Principal ,Department of Computer Science and Engineering,Siddhartha Institute of Technology & Sciences(SITS),Narapally, Korremula Road, Ghatkesar Mandal, Peerzadiguda, Hyderabad, Telangana 500088

Keywords:

Rice Leaf Image, Anisotropic Diffusion Filter Based Unsharp Masking and Crispening, Bayesian Fuzzy Clustering, Vulture Optimization, Auto-Metric Graph Neural Network

Abstract

Plants play an imperious concern for all kinds of life. Out of which, rice plant is considered as a main agricultural crop. These rice plant leaves are furthermore suffered from the disease. The early stage of recognition of disease can overcome the spreading of disease all over rice crops. Several existing methods of Rice Leaf Disease Classification (RLDC) are utilized with Machine Learning (ML), but it does not accurately classify the rice leaf disease, and also it takes high computation time. To overcome these issues, a Vulture-based Auto-metric Graph Neural Network proposed (VAGNN) for RLDC. At first, the input rice leaf images are taken from the Rice Leaf Disease Image Samples dataset. Then the input rice leaf images are pre-processed using Anisotropic Diffusion Filter Based Unsharp Masking and Crispening (ADF-USMC). Then these pre-processed rice leaf images are given to Bayesian Fuzzy Clustering (BFC) for segmentation. Then the segmented image is given into VAGNN to classify the rice leaf disease image namely i) bacterial blight, ii) brown spot, iii) blast and iv) tungro. Finally, the attained outcomes of the designed model are validated with other prevailing models in terms of accuracy, sensitivity, precision, and so on.

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References

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Overall workflow of the proposed VAGNN method

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Published

17.05.2023

How to Cite

Rajasekhar, V. ., Arulselvi, G. ., & Babu, K. S. . (2023). Optimization-Based Auto-Metric Graph Neural Network Framework for Rice Leaf Disease Classification. International Journal of Intelligent Systems and Applications in Engineering, 11(6s), 563–575. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2888

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Research Article

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