International Journal of Electrical and Computer Engineering (IJECE) Vol. 13, No. 2, April 2023, pp. 1914~1921 ISSN: 2088-8708, DOI: 10.11591/ijece.v13i2.pp1914-1921  1914 Journal homepage: http://ijece.iaescore.com Classification of arecanut using machine learning techniques Shabari Shedthi Billadi 1 , Madappa Siddappa 2 , Surendra Shetty 3 , Vidyasagar Shetty 4 1 Department of Computer Science and Engineering, NMAM Institute of Technology-Affiliated to NITTE (Deemed to be University), Nitte, Karnataka, India 2 Department Computer Science and Engineering, Sri Siddhartha Institute of Technology, Tumkur, Karnataka, India 3 Department of Master of Computer Applications, NMAM Institute of Technology-Affiliated to NITTE (Deemed to be University), Nitte, Karnataka, India 4 Department of Mechanical Engineering, NMAM Institute of Technology-Affiliated to NITTE (Deemed to be University), Nitte, Karnataka, India Article Info ABSTRACT Article history: Received Feb 3, 2022 Revised Oct 6, 2022 Accepted Nov 1, 2022 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%. Keywords: Agriculture Arecanut Classifying Image processing Machine learning This is an open access article under the CC BY-SA license. Corresponding Author: Shabari Shedthi Billadi Department of Computer Science and Engineering, NMAM Institute of Technology-Affiliated to NITTE (Deemed to be University) Nitte 574110, Karnataka, India Email: shabarishetty87@gmail.com 1. INTRODUCTION The use of technology in agriculture was initially used for simple and precise calculations, which were found to be relatively difficult in manual calculations. In the next generation, research decision support systems will be developed to take tactical decisions on agricultural production and protection. Arecanut is a major cash crop in the undivided Dakshina Kannada district and Malnad region. Areca Catechu Linn is the scientific name of the arecanut and it is also called betelnut in India. In India, the cultivation and use of arecanut have their own unique practice [1]. In the food processing industry nowadays, there is a requirement for the production of quality products at a very fast rate, so developing an expert system helps to make decisions in less time. In manual grading, individual person perception makes differences in identifying whether a product is defective or healthy, but this machine vision framework will decrease such human errors and help to perform at a faster rate. Arecanut is used for making supari, areca tea, and paint. It has its own value in several religious ceremonies. In the Indian’s ancient medicine system book of i.e., Dhanwantari Nighantu, it mentioned the use of arecanut as one of the five natural aromatics (panchasugandhikam) along with pepper, clove, nutmeg,