Clustering and Classification Algorithms in Food and Agricultural Applications: A Survey

Radnaabazar Chinchuluun, Won Suk Lee, Jevin Bhorania and Panos M. Pardalos ()
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Panos M. Pardalos: University of Florida

A chapter in Advances in Modeling Agricultural Systems, 2009, pp 433-454 from Springer

Abstract: Abstract Data mining has become an important tool for information analysis in many disciplines. Data clustering, also known as unsupervised classification, is a popular data-mining technique. Clustering is a very challenging task because of little or no prior knowledge. Literature review reveals researchers’ interest in development of efficient clustering algorithms and their application to a variety of real-life situations. This chapter presents fundamental concepts of widely used classification algorithms including k-means, k-nearest neighbor, artificial neural networks, and fuzzy c-means. We also discuss applications of these algorithms in food and agriculture sciences including fruits classification, machine vision, wine classification, and analysis of remotely sensed forest images.

Keywords: Artificial Neural Network; Synthetic Aperture Radar; Machine Vision; Citrus Fruit; Probabilistic Neural Network (search for similar items in EconPapers)
Date: 2009
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DOI: 10.1007/978-0-387-75181-8_21

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