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All Journal International Journal of Electrical and Computer Engineering
Shabari Shedthi Billadi
NMAM Institute of Technology-Affiliated to NITTE (Deemed to be University)
Computer Science & IT Electrical & Electronics Engineering

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Classification of arecanut using machine learning techniques Shabari Shedthi Billadi; Madappa Siddappa; Surendra Shetty; Vidyasagar Shetty
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1914-1921

Abstract

In agricultural domain research, image processing and machine learning techniques play an important role. This paper provides a unique solution for classifying the good and defective arecanuts based on their color, texture, and density value. In the market different varieties of arecanut are available. Usually, qualitative sorting is done manually, and this can be replaced by applying machine vision techniques to grade the arecanut. Classification of arecanut based on quality is done using various machine learning techniques and it is observed that artificial neural networks give good results compared to other classifiers like logistic regression, k-nearest neighbor, naive Bayes classifiers, and support vector machine. A unique density feature is considered here for better classification. The result of classifiers without considering the density feature is compared with respect to the density feature and it is observed that artificial neural networks work better than the others. The proposed method works effectively for classifying arecanut with an accuracy of 98.8%.
Co-Authors Madappa Siddappa Surendra Shetty Vidyasagar Shetty