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Drones
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Advances in UAV-Based Remote Sensing for Climate-Smart Agriculture
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Advances in UAV-Based Remote Sensing for Climate-Smart Agriculture

A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drones in Agriculture and Forestry".

Deadline for manuscript submissions: closed (14 April 2026) | Viewed by 2500

Special Issue Editors

Dr. Hongquan Wang

E-Mail Website
Guest Editor
Agriculture and Agri-Food Canada, Ottawa, ON, Canada
Interests: remote sensing; proximal sensing; precision agriculture; hydrology; forestry; climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Taifeng Dong

E-Mail Website
Guest Editor
National Wildlife Research Centre, Environment and Climate Change Canada, 1125 Colonel By Drive, Ottawa, ON K1A0H3, Canada
Interests: remote sensing application in agriculture; bio-geophysical properties estimation; crop phenology; data assimilation
Special Issues, Collections and Topics in MDPI journals
Dr. Liming He

E-Mail Website
Guest Editor
Canada Centre for Remote Sensing, Natural Resources Canada, Ottawa, ON, Canada
Interests: remote sensing; agriculture; water resources; biogeochemical cycles; climate change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unmanned Aerial Vehicles (UAVs) bridge the gap between satellite-derived data (with its broad spatiotemporal coverage) and ground-based point-scale measurements, offering unprecedented resolution for monitoring agricultural systems. Climate-Smart Agriculture (CSA), first proposed in 2010 by the FAO, aims to sustainably boost agricultural productivity, enhance resilience to climate changes, and reduce greenhouse gas emissions. Over the last decade, CSA has emerged as a critical framework to address food security and environmental challenges. By integrating multi-scale observations from satellites, UAVs, proximal sensing, and ground-based systems with advanced data analytics (e.g., data assimilation, artificial intelligence, and big data architecture), crop-growth models, and high-performance computing, UAVs are uniquely positioned to accelerate CSA’s transformative goals. These technologies enable real-time monitoring, precision agricultural practices, and adaptive, data-driven decision-making, thus empowering stakeholders to optimize resource use and mitigate environmental impacts in a rapidly changing climate.

This Special Issue aims to gather cutting-edge advances of UAV research and development to directly support CSA. The scope of this Special Issue includes, but is not limited to, the following areas:

  1. Crop breeding and phenotyping:

Advanced approaches that leverage UAV data (e.g., hyperspectral, multispectral and LiDAR sensors) for real-time monitoring of phenotypic traits (e.g., leaf chlorophyll/nitrogen content, plant height, phenology and biomass) in breeding trails, accelerating the development of climate-resilient crop varieties.

  1. Precision agricultural managements:

Investigations into the use of UAVs for site-specific managements and early detection of climate-induced stress (e.g., drought, flooding, pests, and lodging). This includes enabling variable-rate applications of seeding, fertilizer and water, and adaptations strategies of cover cropping and tillage, reducing cost and waste while maintaining/maximizing yield.

  1. Emissions and environment impact assessments:

Applications that integrate UAV with radiative transfer models, process-based models, and AI for mapping soil carbon, monitoring methane emissions, and quantifying carbon sequestration.

We welcome interdisciplinary submissions that combine remote sensing, agronomy, climate science, and data science to support CSA at different scales.

You may choose our Joint Special Issue in Remote Sensing.

Dr. Hongquan Wang
Dr. Liming He
Dr. Taifeng Dong
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. Drones 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 2600 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

  • unmanned aerial vehicles
  • remote sensing
  • climate-smart agriculture
  • crop phenotyping
  • precision agriculture

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Published Papers (3 papers)

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Research

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26 pages, 3028 KB  
Article
A Multi-Sensor UAV Platform: Design, Testing, and Application for High-Throughput Plant Phenotyping
by Liyike Ji, Xu Wang, Hani Hassan and Zhanao Deng
Drones 2026, 10(5), 372; https://doi.org/10.3390/drones10050372 - 13 May 2026
Viewed by 553
Abstract
Unmanned aerial vehicles (UAVs) are broadly used for high-throughput plant phenotyping, yet their long-term use in public-sector research is increasingly challenged by regulatory restrictions and reliance on proprietary platforms. This study presented a regulation-compliant, modular multi-sensor unmanned aerial system (UAS) designed to deliver [...] Read more.
Unmanned aerial vehicles (UAVs) are broadly used for high-throughput plant phenotyping, yet their long-term use in public-sector research is increasingly challenged by regulatory restrictions and reliance on proprietary platforms. This study presented a regulation-compliant, modular multi-sensor unmanned aerial system (UAS) designed to deliver flexible, high-quality phenotyping data without dependence on restricted ecosystems. A dual-mount, open-architecture payload integrated RGB, multispectral, and thermal sensors, enabling simultaneous acquisition of structural, spectral, and thermal information within a unified workflow. Field validation in a lantana (Lantana camara) breeding trial demonstrated high-precision multi-sensor data fusion and reliable trait extraction. Spatial co-registration achieved centimeter-level accuracy, with alignment errors of 0.88 cm (multispectral) and 3.23 cm (thermal) relative to the RGB reference. UAV-derived canopy height closely matched ground measurements (R2 up to 0.98; RMSE as low as 1.57 cm), while canopy coverage estimates showed consistency across sensing modalities (R2 = 0.99; RMSE = 0.02 m2). Calibrated thermal orthomosaics provided robust canopy temperature estimation (RMSE = 3.13 °C), supporting a quantitative assessment of plant physiological status. Together, these results demonstrate that a regulation-compliant, open-architecture UAV platform can achieve high accuracy in multi-modal phenotyping while maintaining flexibility and cost efficiency. This work demonstrates a scalable and sustainable framework for UAV-based phenotyping, enabling researchers to adapt to evolving regulations while advancing data-driven crop improvement. Full article
(This article belongs to the Special Issue Advances in UAV-Based Remote Sensing for Climate-Smart Agriculture)
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Review

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35 pages, 1068 KB  
Review
UAV-Based Remote Sensing and Artificial Intelligence for Climate-Smart Agriculture: A Systematic Review of Technologies, Analytics, and Applications in Smallholder Systems
by Andrew Manu, Jeff Dacosta Osei and Thomas Lawler
Drones 2026, 10(6), 451; https://doi.org/10.3390/drones10060451 - 9 Jun 2026
Viewed by 184
Abstract
Unmanned aerial vehicle (UAV)-based remote sensing combined with artificial intelligence (AI) has emerged as a key enabler of climate-smart agriculture (CSA). However, the extent to which these technologies operationalize CSA’s three pillars, productivity, adaptation, and mitigation, remains unevenly assessed. This study presents a [...] Read more.
Unmanned aerial vehicle (UAV)-based remote sensing combined with artificial intelligence (AI) has emerged as a key enabler of climate-smart agriculture (CSA). However, the extent to which these technologies operationalize CSA’s three pillars, productivity, adaptation, and mitigation, remains unevenly assessed. This study presents a PRISMA-guided systematic review of 59 peer-reviewed studies examining UAV–AI applications in agricultural systems. The synthesis categorizes platform configurations, sensor modalities, analytical architectures, geographic distribution, and data integration strategies, and evaluates their alignment with CSA objectives. Results indicate that productivity-oriented applications, including yield estimation, biomass mapping, and nutrient assessment, are the most mature, while adaptation-focused stress detection is also well established. In contrast, mitigation-oriented applications, such as carbon quantification and greenhouse gas monitoring, remain comparatively underrepresented. The analysis further reveals a growing convergence toward multimodal sensing and cross-scale data integration linking UAV observations with satellite and environmental datasets. However, substantial variability in validation approaches and dataset representativeness limits generalizability and scalability. Advancing UAV–AI contributions to CSA therefore requires methodological standardization, interoperable data governance, and strengthened institutional capacity. Collectively, the findings position UAV–AI systems as emerging components of climate-smart agricultural intelligence infrastructure rather than isolated monitoring tools. Full article
(This article belongs to the Special Issue Advances in UAV-Based Remote Sensing for Climate-Smart Agriculture)
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Other

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19 pages, 4121 KB  
Technical Note
drone2report: A Configuration-Driven Multi-Sensor Batch-Processing Engine for UAV-Based Plot Analysis in Precision Agriculture
by Nelson Nazzicari, Giulia Moscatelli, Agostino Fricano, Elisabetta Frascaroli, Roshan Paudel, Eder Groli, Paolo De Franceschi, Giorgia Carletti, Nicolò Franguelli and Filippo Biscarini
Drones 2026, 10(4), 301; https://doi.org/10.3390/drones10040301 - 18 Apr 2026
Viewed by 1025
Abstract
Unmanned aerial vehicles (UAVs) have become indispensable tools in precision agriculture and plant phenotyping, enabling the rapid, non-destructive assessment of crop traits across space and time. Equipped with RGB, multispectral, thermal, and other sensors, UAVs provide detailed information on canopy structure, physiology, and [...] Read more.
Unmanned aerial vehicles (UAVs) have become indispensable tools in precision agriculture and plant phenotyping, enabling the rapid, non-destructive assessment of crop traits across space and time. Equipped with RGB, multispectral, thermal, and other sensors, UAVs provide detailed information on canopy structure, physiology, and stress responses that can guide management decisions and accelerate breeding programs. Despite these advances, the downstream processing of UAV imagery remains technically demanding. Converting orthomosaics into standardized, biologically meaningful data often requires a combination of photogrammetry, geospatial analysis, and custom scripting, which can limit reproducibility and accessibility across research groups. We present drone2report, an open-source python-based software that processes orthomosaics from UAV flights to generate vegetation indices, summary statistics, derived subimages, and text (html) reports, supporting both research and applied crop breeding needs. Alongside the basic structure and functioning of drone2report, we also present five case studies that illustrate practical applications common in UAV-/drone-phenotyping of plants: (i) thresholding to remove background noise and highlight regions of interest; (ii) monitoring plant phenotypes over time; (iii) extracting information on plant height to detect events like lodging or the falling over of spikes; (iv) integrating multiple sensors (cameras) to construct and optimize new synthetic indices; (v) integrate a trained deep learning network to implement a classification task. These examples demonstrate the tool’s ability to automate analysis, integrate heterogeneous data and models, and support reproducible computation of agronomically relevant traits. drone2report streamlines orthorectified UAV-image processing for precision agriculture by linking orthomosaics to standardized, plot-level outputs. Its modular, configuration-driven design allows transparent workflows, easy customization, and integration of multiple sensors within a unified analytical framework. By facilitating reproducible, multi-modal image analysis, drone2report lowers technical barriers to UAV-based phenotyping and opens the way to robust, data-driven crop monitoring and breeding applications. Full article
(This article belongs to the Special Issue Advances in UAV-Based Remote Sensing for Climate-Smart Agriculture)
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