The integration of optical sensors and machine learning (ML) technologies has transformed agricultural monitoring, delivering precise, real-time insights into crop health, growth patterns, and environmental interactions. These sensors-spanning multispectral, hyperspectral, and RGB cameras-capture intricate spectral signatures that detect subtle physiological shifts in crops. When paired with ML algorithms, the resulting data streams yield actionable intelligence, informing key decisions in precision agriculture, yield forecasting, and disease control.
This Reprint of the second edition of the Special Issue entitled "Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring" builds upon the foundational research established by prior studies in the field. Featuring 17 original research articles, this collection tackles key methodological challenges, including data imbalance, improved model transferability, and the fusion of multisensor data for resilient monitoring. Together, these works highlight the advancing capabilities of optical sensors in acquiring high-resolution, multidimensional datasets, which ML models exploit for advanced pattern recognition and predictive modeling. Beyond refining current approaches, the studies within this Reprint pave the way for emerging innovations, such as edge computing and AI-powered automation in agricultural ecosystems.
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