Performance Evaluation of VGG models in Detection of Wheat Rust

Authors

  • Rajwinder Singh Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology (Degree Wing), Sector-26, Chandigarh Affiliated to Panjab University, Chandigarh, India
  • Rahul Rana Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology (Degree Wing), Sector-26, Chandigarh Affiliated to Panjab University, Chandigarh, India
  • Sunil Kr. Singh Department of Computer Science and Engineering, Chandigarh College of Engineering and Technology (Degree Wing), Sector-26, Chandigarh Affiliated to Panjab University, Chandigarh, India

DOI:

https://doi.org/10.51983/ajcst-2018.7.3.1892

Keywords:

Deep Learning, Wheat Rust, Pre-Trained Models, Performance Evaluation, Crop Disease Detection

Abstract

The agricultural sector is the backbone of Indian economy and social development but due to lack of awareness towards crop management, a large number of crops get wasted each year. Automated Systems are required for this purpose. This paper tries to highlight the efficiency of two existing models of deep learning, VGG16 and VGG19 for proper detection of wheat rust disease in the infected wheat crop. These two models use convolutional neural networks for image classification and which can be used to design an intelligent system which can easily detect wheat rust in crop images. This paper basically presents the comparative analysis of the accuracy and efficiency along with usability to select the best model for systems that can be used for crop safety.

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Published

05-11-2018

How to Cite

Singh, R., Rana, R., & Singh, S. K. (2018). Performance Evaluation of VGG models in Detection of Wheat Rust. Asian Journal of Computer Science and Technology, 7(3), 76–81. https://doi.org/10.51983/ajcst-2018.7.3.1892