Agriculture is increasingly threatened by diverse global security challenges, including
climate change, biodiversity loss, and health security risks, underscoring the need
for sustainable practices to ensure food security for future generations. Advanced
technologies such as quantum machine learning (QML) offer transformative potential
in addressing these challenges by enhancing crop disease monitoring systems. This
paper explores the implementation of QML in agriculture, with a particular focus on
its application to crop disease detection and prevention. Leveraging image processing,
sensory data analysis, and spectral imaging, QML algorithms demonstrate superior accuracy
and efficiency in early disease recognition, surpassing traditional methods. A case
study of Hungary's crop disease monitoring system illustrates the practical benefits
of integrating QML with technologies such as remote sensing and IoT. By enabling real-time
monitoring and predictive analytics, QML not only aids in mitigating crop losses but
also contributes to sustainable agricultural practices and global food security. Despite
current limitations in quantum hardware, advancements in QML present promising opportunities
for revolutionizing agricultural systems and ensuring resilience against evolving
threats.
