Gépi tanulás, statisztikus adatfeldolgozás, jelfeldolgozáson alapuló alkalmazások
(pl. beszéd, kép, videó)
Információbiztonság
Informatika és információs rendszerek
Informatikai biztonság
Mesterséges intelligencia és döntéstámogatás
Mezőgazdaság
Agriculture faces increasing risks in ensuring global food security, with challenges
such as climate change, biodiversity loss, and plant disease and pest outbreaks posing
significant threats to crop yields. These factors are compounded by growing global
populations, resource limitations, and market fluctuations. Traditional methods for
crop pest and disease monitoring, often reliant on manual surveys and basic machine
learning models, are inadequate in addressing these challenges due to limited scalability
and accuracy. This research explores the potential application of quantum machine
learning (QML) for crop pest and disease monitoring, leveraging the advanced computational
capabilities of quantum computers to improve detection accuracy, efficiency, and scalability.
The study reviews existing monitoring systems, recent advancements in quantum computing
technologies, and the integration of QML algorithms, which offer promising solutions
for handling large, complex agricultural datasets. While the practical application
of QML in agriculture is still in its early stages, its theoretical advantages indicate
significant potential for future smart agricultural systems.
