Vehicle Color-Type Detection Based on YOLOv8 and K-Means

There is provided a computing device for managing honeybee colonies for pollination of crop(s), comprises: a processor(s) configured for: for each one of a plurality of honeybee colonies positioned for pollination of crop(s) in a geographical area: obtaining in-colony feature(s), computed based on output of internal sensor(s) monitoring the honeybee colony, wherein the in-colony feature(s) is indicative of an internal state of the honeybee colony positioned for pollination of crop(s) in the geographical area, obtaining out-colony feature(s), computed based on output of external sensor(s) monitoring the environment of the honeybee colony, wherein the out-colony feature is indicative of an external environment of the honeybee colony, feeding a combination of the in-colony feature(s) and the out-colony feature(s) into a machine learning model, and obtaining an outcome indicating pollination effectiveness of the honeybee colony.

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Publication Details

Journal
Journal of Physical Chemistry and Functional Materials
Published
2026-06-29
DOI
https://doi.org/10.54565/jphcfum.1951468
Primary Topic
Insect and Arachnid Ecology and Behavior
Type
article
Field-Weighted Citation Impact
0.00

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article

Vehicle Color-Type Detection Based on YOLOv8 and K-Means

Orhan Yaman, İrfan Kılıç, Mehmet Karaköse, Nafiye Nur Apaydın et al.
Journal of Physical Chemistry and Functional Materials
Insect and Arachnid Ecology and Behavior
article

Vehicle Color-Type Detection Based on YOLOv8 and K-Means

Orhan Yaman, İrfan Kılıç, Mehmet Karaköse, Nafiye Nur Apaydın, İrem Çiçek
article en

Abstract

There is provided a computing device for managing honeybee colonies for pollination of crop(s), comprises: a processor(s) configured for: for each one of a plurality of honeybee colonies positioned for pollination of crop(s) in a geographical area: obtaining in-colony feature(s), computed based on output of internal sensor(s) monitoring the honeybee colony, wherein the in-colony feature(s) is indicative of an internal state of the honeybee colony positioned for pollination of crop(s) in the geographical area, obtaining out-colony feature(s), computed based on output of external sensor(s) monitoring the environment of the honeybee colony, wherein the out-colony feature is indicative of an external environment of the honeybee colony, feeding a combination of the in-colony feature(s) and the out-colony feature(s) into a machine learning model, and obtaining an outcome indicating pollination effectiveness of the honeybee colony.

Journal of Physical Chemistry and Functional MaterialsVol. 9(1)
Fırat University (TR)
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu
Openalex Percentile: Top 8%
Insect and Arachnid Ecology and Behavior
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Vehicle Color-Type Detection Based on YOLOv8 and K-Means — Orhan Yaman, İrfan Kılıç, et al. · Journal of Physical Chemistry and Functional Materials (2026) | TGRS Research Map | TGRS