@inbook{f909ad005b9c4dd881ed42c9e3dabfc7,
title = "Insect Collision Detection Using Machine Learning with Correlation to Climatic Conditions",
abstract = "Insects have experienced dramatic declines over the past 20 years. This has driven the need to monitor populations in relation to the impact of weather conditions and mitigations to conserve insect communities. A machine learning approach is introduced to detect insect collisions on car number plates by considering the differences between number plate images at the start and end of a journey. This is subsequently correlated with various weather conditions and journey parameters to monitor changing insect populations. The present approach does not impose stringent requirements on the resolution/format of plate images, nor involves application of physical frames for image collection.",
keywords = "image analysis, insect monitoring, machine learning, number plate recognition, pattern recognition",
author = "Tudur David and Matt Jones and Paul Cross and Cristiano Palego",
year = "2022",
month = jun,
day = "6",
doi = "10.1109/EAIS51927.2022.9787692",
language = "English",
series = "IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)",
publisher = "IEEE",
booktitle = "202022 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)",
}