Machine Vision and Unmanned Systems for Data-Driven and Sustainable Agricultural Monitoring and Management
A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Computer Applications and Artificial Intelligence in Agriculture".
Deadline for manuscript submissions: 31 May 2027 | Viewed by 96
Special Issue Editors
Interests: conservative organic farming systems; soil conservation and no tillage; machines for soil tillage; under-row weed control in vineyard; precision agriculture; autonomous robot for agriculture; sustainable vineyard management; weed control; machines for physical weed control; robotic and digital fleet management; soil disinfection with physical method
Special Issues, Collections and Topics in MDPI journals
Interests: under-row weed control in vineyards; conservative organic farming systems; soil conservation and no tillage; mechanical under-row weed control; precision agriculture; autonomous robot for agriculture; sustainable vineyard management; sustainable soil and weed management in orchards; weed control; biodiversity; autonomous mower; sustainable turfgrass management
Special Issues, Collections and Topics in MDPI journals
Interests: precision agriculture; autonomous robot for agriculture; image analysis; artificial intelligence; machine vision; machine learning; benchmarking for agricultural robot performance; sustainable turfgrass management; weed control; biodiversity; autonomous mower; digitalization; remote sensing systems
Special Issue Information
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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AgriEngineering is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- machine vision
- unmanned systems
- precision agriculture
- agricultural monitoring
- autonomous robotics
- decision support
- deep learning
- computer vision
- UAV
- UGV
- environmental monitoring
- biodiversity assessment
- ecosystem health
- sustainable farming systems
- artificial intelligence
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