Optimization of Fruit Disease Detection Process: Using Gaussian Filtering Along With Enhanced SVM

Authors

  • Hardeep Singh Student, Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, Punjab, India
  • Sandeep Sharma HOD, Department of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, Punjab, India

DOI:

https://doi.org/10.51983/ajcst-2018.7.2.1876

Keywords:

Gaussian Smoothening, Weighted Kernel function, Enhanced SVM, Prediction Accuracy, Mean or Average Error

Abstract

Fruit disease detection becomes critical since economic and related issues are influenced through the healthy and non-healthy fruits. Technology has advanced and is used to primarily detect and abnormality which is not visible through the naked eye. This paper proposes a new technique of fruit disease detection at early stage for which Gaussian smoothening is used at pre-processing stage along with weighted kernel function within SVM for achieving higher classification accuracy. Feature extraction and selection mechanism uses rank based mechanism that allocates ranks on the basis of predictive significance. The result is obtained in terms of prediction accuracy and mean or average error. Result is optimized by the factor of 10%.

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Published

05-08-2018

How to Cite

Singh, H., & Sharma, S. (2018). Optimization of Fruit Disease Detection Process: Using Gaussian Filtering Along With Enhanced SVM. Asian Journal of Computer Science and Technology, 7(2), 18–20. https://doi.org/10.51983/ajcst-2018.7.2.1876