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Journals / Journal of Physical Chemistry and Functional Materials / 2026 / Volume: 9 Issue: 1

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

İrem Çiçek(FIRAT UNIVERSITY)0009-0001-4932-8969
Nafiye Nur Apaydın(FIRAT UNIVERSITY)0009-0006-3438-7401
İrfan Kılıç(FIRAT UNIVERSITY)0000-0001-5079-2825
Orhan Yaman(FIRAT UNIVERSITY)0000-0001-9623-2284
Mehmet Karaköse(FIRAT UNIVERSITY)0000-0002-3276-3788
Pages
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Publication Date
2026-06-29
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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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Key Findings & Data: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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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Vehicle color and type detections · Traffic Road Object Detection Dataset · YOLOv8

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.

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Cite This Article

Çiçek, İ., Apaydın, N. N., Kılıç, İ., Yaman, O., Karaköse, M. (2026). Vehicle Color-Type Detection Based on YOLOv8 and K-Means. Journal of Physical Chemistry and Functional Materials, Volume: 9 Issue: 1. https://doi.org/10.54565/jphcfum.1951468

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