Machine Vision and Unmanned Systems for Data-Driven and Sustainable Agricultural Monitoring and Management

Dear Colleagues,

The ongoing digital transformation in agriculture is progressively reshaping traditional production systems toward data-driven, automated, and sustainable management models. Within this context, machine vision and unmanned systems are emerging as key enabling technologies, providing advanced tools for real-time monitoring, diagnosis, and decision support across diverse agricultural scenarios. The integration of high-resolution imaging sensors, artificial intelligence, and autonomous platforms, such as UAVs and UGVs, enhances the capability to capture, process, and interpret complex agronomic and environmental information. Machine vision, combined with deep learning and computer vision algorithms, allows for the automated detection of plant species, biotic and abiotic stresses, enabling targeted and efficient interventions. When embedded in unmanned robotic systems, these technologies facilitate autonomous navigation, site-specific management, and precision operations, ultimately contributing to improved productivity, reduced environmental impact, and comprehensive environmental monitoring, thereby supporting the transition toward more sustainable agricultural systems.

This Special Issue aims to collect cutting-edge research contributions, methodological developments, and practical applications related to the use of machine vision and unmanned systems for agricultural monitoring, decision-support processes, and autonomous field operations. Topics of interest include precision agriculture, crop and environmental monitoring, biodiversity assessment, yield prediction, and resource optimization, with a focus on sustainability and automation.

Prof. Dr. Michele Raffaelli
Dr. Lorenzo Gagliardi
Dr. Sofia Matilde Luglio
Guest Editors

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