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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File | Size | Format | |
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tsimpirisCES1-2023.pdf | 786.14 kB | Adobe PDF | View/Open |
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