Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/31672
Title: Smart agriculture: Predictive height analysis for universal crop health classification
Authors: Tsakiridis, Sotirios 
Papaioannou, Nikolaos 
Tsimpiris, Alkiviadis 
Varsamis, Dimitrios 
Papakonstantinou, Apostolos 
Major Field of Science: Engineering and Technology
Field Category: Electrical Engineering - Electronic Engineering - Information Engineering
Keywords: Unsupervised Learning;UAV;Smart Agriculture;Clustering
Issue Date: 2023
Source: Contemporary Engineering Sciences, 2023, vol. 16, no. 1, pp. 55-70
Volume: 16
Issue: 1
Journal: Contemporary Engineering Sciences 
Abstract: The use of contemporary information and communication technol- ogy to maximize agricultural output while reducing labor costs is known as ”smart agriculture”. This term is becoming more and more prevalent. The primary challenge in the agricultural sector lies in the vastness of crops, coupled with varied topography and soil instability, making con- trol challenging. In this paper, a system for determining the average predicted height of healthy plants at a given growth stage is proposed and evaluated. Based on this height, we then classify agricultural plants as healthy or unhealthy. It’s important to note that our system works with any crop kind and growth stage.
URI: https://hdl.handle.net/20.500.14279/31672
ISSN: 13147641
DOI: 10.12988/ces.2023.93115
Rights: Creative Commons by-nc-nd Attribution License
Type: Article
Affiliation : Cyprus University of Technology 
International Hellenic University 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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