Integrating UAV-Based and Multi-Source Remote Sensing with Artificial Intelligence for Better Crop Management
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".
Deadline for manuscript submissions: 15 August 2026 | Viewed by 47
Special Issue Editors
Interests: marine remote sensing; photogrammetry; aquatic ecological monitoring; benthic mapping; coral reef monitoring; UAV applications; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; ecological monitoring; biodiversity and species distribution; object detection; land cover classification; statistical modeling and simulation
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; coral reef monitoring; underwater image processing; photogrammetry; aquatic ecological monitoring; IoT for aquatic ecologial monitoring
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Unmanned Aerial Vehicles (UAVs) have become a transformative platform in agricultural remote sensing, offering high flexibility, ultra-high spatial resolution, and timely field-scale data acquisition. As agriculture faces pressures from climate change and resource constraints, precise crop management is increasingly critical. Traditional methods such as field surveys and satellite observations are often limited in resolution or frequency, especially in heterogeneous landscapes. UAVs bridge this gap by enabling detailed monitoring of crop growth, health, and stress throughout the season. Equipped with multispectral, thermal, and other sensors, they capture crop structure and physiological information essential for precision agriculture applications such as variable-rate fertilization, irrigation optimization, and pest detection. Advances in AI and data processing enhance the extraction of agronomic insights from UAV imagery. Integrated with ground data and models, UAV-derived products support multi-scale crop management and decision-making, positioning UAV technology as a key research frontier in agricultural remote sensing.
This Special Issue provides a comprehensive overview of UAV-based remote sensing in crop management across diverse cropping systems and agro-ecologies. It highlights methodological advances, innovative applications, and case studies demonstrating UAV data’s role in improving productivity, resource efficiency, and sustainability. Its scope aligns with that of Remote Sensing by focusing on remote sensing technologies, data analysis, and integrated frameworks. Contributions should emphasize UAV observations while exploring synergies with satellite data, ground measurements, and decision-support systems. This Special Issue bridges technical innovation with agricultural applications to support farmers, researchers, and policymakers.
This Special Issue welcomes original research articles, review papers, and methodological studies. Suggested themes include, but are not limited to, UAV-based monitoring of crop growth, phenology, and spatial variability; crop health, stress, disease, and pest detection using multispectral, hyperspectral, or thermal UAV data; the estimation of crop biophysical parameters (e.g., LAI, biomass, canopy height, nitrogen status); UAV applications in irrigation management, water stress assessment, and evapotranspiration estimation; yield estimation and in-season yield forecasting using UAV imagery; machine learning and deep learning methods for crop mapping and trait extraction; multi-sensor and multi-platform data fusion (UAV–satellite–ground integration); and UAV-supported precision agriculture and variable-rate management strategies.
Dr. Fan Zhao
Dr. Kangning Cui
Dr. Jiaqi Wang
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.
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Keywords
- UAV
- drone-based remote sensing
- crop management
- precision agriculture
- crop monitoring
- deep learning and machine learning
- multispectral and hyperspectral imagery
- agricultural decision support
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