Application of Remote Sensing and Machine Learning in Sustainable Agriculture
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Agriculture".
Deadline for manuscript submissions: 30 April 2025 | Viewed by 967
Special Issue Editors
Interests: GIS; remote sensing; cartography; image processing and analysis, precision agriculture
Special Issues, Collections and Topics in MDPI journals
Interests: identifying trends and monitoring and modeling changes based on remote sensing data; geospatial modeling integrating deep learning (DL); machine learning (ML) and AI techniques; integrated multisource geospatial data management for the digital twin; designing web applications for geospatial data management; advanced 3D/4D urban modeling using geospatial technologies
Interests: remote sensing; GIS; GPR; geosensor networks; geospatial data processing
Interests: topography; construction survey; mapping; cadastre; UAV photogrammetry; GIS
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Currently, geospatial data are at the core of much research in various fields, such as geodesy, civil engineering, environment, precision agriculture, forestry, etc. The evolution of geospatial technologies, as well as open source data, can support various studies and research that support the sustainable development of the society in which we live. The processes involved in the precise monitoring of plant health in order to increase agricultural productivity are essential for food security and economic sustainability. The use of remote sensing techniques in agricultural land monitoring processes is an increasingly used technique for modernizing conventional methods and agricultural strategies worldwide. These geospatial technologies are very useful for forecasting the yield and management of various agricultural crops, regardless of their location. These new technologies (platforms, sensors, and algorithms) allow for the low-cost, but high-resolution, observation of agricultural crops in order to obtain accurate information about their health status, as well as an increase in productivity using modern technologies in the machine learning field. In this context, this Special Issue aims to promote high-quality articles regarding recent activity in the field of remote sensing, artificial intelligence, and machine learning with practical applicability in the agricultural field.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but need not be limited to) the following:
- Land surveying in the agricultural field, from the established topographic field instrumentations (total stations, GNSS systems, TLS, etc.) to the new methods based on optical remote sensing (UAV or airborne platforms for LiDAR and digital photogrammetry, InSAR, satellite images, etc.);
- Challenges and advances in agriculture;
- Integrating models, methods, techniques, and tools for geospatial applications in agricultural field;
- GIS applications in agriculture management, policy, and decision making;
- Precision agriculture;
- Integration of field data and sensors in decision support systems;
- Techniques for vegetation structure modelling and biomass assessment;
- Land crops dynamics and the environmental/ecological implications;
- Artificial intelligence and machine learning;
- Big data analysis;
- Crop nutrition diagnosis;
- Crop monitoring;
- Smart agriculture;
- Best practices, guidelines, and planning using acquired geospatial data.
I/We look forward to receiving your contributions.
Dr. Mihai Valentin Herbei
Prof. Dr. Ana Cornelia Badea
Prof. Dr. Aleksandar Ristić
Dr. Paul Sestras
Guest Editors
Manuscript Submission Information
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Keywords
- remote sensing
- artificial intelligence
- machine learning
- crop monitoring
- smart agriculture
- UAV
- sensors
- spatial–temporal analysis
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