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Applications of Unmanned Aerial Remote Sensing in Precision Agriculture

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: 10 November 2025 | Viewed by 705

Special Issue Editors


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Guest Editor
Department of Civil and Environmental Engineering, University of Florence, Via S. Marta 3, 50139 Florence, Italy
Interests: geomatics; photogrammetry; remote sensing; UAS; sensors; cultural heritage; precision agriculture; climate change; thermal imaging; terraced landscapes; multimedia tools for education

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Florence, Via S. Marta 3, 50139 Florence, Italy
Interests: navigation and positioning; attitude and pose estimation; 3D modeling; geomatics; sensors; deep learning; computer vision; climate change; cultural heritage preservation; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the cutting-edge intersection of unmanned aerial systems (UASs) and precision agriculture, aiming to bring together a collection of contributions from leading researchers. The goal is to highlight the transformative role that UAS technology plays in enhancing agricultural practices through precise and detailed remote sensing capabilities.

The Special Issue will outline innovative methodologies, algorithms and applications that leverage the capabilities of UASs for precision agriculture, ranging from crop monitoring and health assessment to soil analysis and irrigation management, exploring how they can provide farmers and agronomists with critical insights and tailored information to optimize agricultural outputs and sustainability.

Researchers and practitioners in fields such as remote sensing, agronomy, environmental science and agricultural engineering will find valuable topics and methodologies to enhance their studies and knowledge in this rapidly evolving interdisciplinary domain.

The topics of interest may include, but are not limited to, the following:

  • Crop health monitoring and stress detection;
  • Precision irrigation and water management;
  • Soil moisture and nutrient mapping;
  • Weed and pest detection and management;
  • Yield estimation and forecasting;
  • Multi-spectral, hyper-spectral and thermal imaging applications;
  • UAS-based 3D terrain modeling and mapping;
  • Temporal analysis for crop growth monitoring;
  • Integration of UAS data with ground-based sensors;
  • Machine learning and AI for agricultural data analysis;
  • Decision support systems for farm management;
  • Environmental impact assessment;
  • Drought and disease early warning systems;
  • UAV flight planning and mission control for agriculture;
  • Data fusion techniques combining UAS and satellite imagery;
  • Case studies and field experiments;
  • Climate change mitigation strategies.

The Special Issue fits within the scope of the journal Remote Sensing as it explores the integration of advanced remote sensing technologies with precision agriculture practices. In fact, unmanned aerial systems allow for capturing high-resolution, multi-dimensional data from agricultural environments, confirming their potential in revolutionize agricultural practices through detailed, timely and accurate data collection and analysis.

This Special Issue addresses how these data can significantly enhance agricultural efficiency, sustainability and productivity. As such, this Special Issue aims to contribute to the broader mission of Remote Sensing in advancing the science and technology of remote sensing applications.

Dr. Erica Isabella Parisi
Dr. Fabiana Di Ciaccio
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • precision agriculture
  • unmanned aerial systems (UASs)
  • remote sensing
  • crop monitoring
  • soil analysis
  • irrigation management
  • multi-spectral imaging
  • hyper-spectral imaging
  • thermal imaging
  • machine learning
  • environmental impact
  • data fusion

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Published Papers (1 paper)

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Research

17 pages, 9448 KiB  
Article
Plant Height and Soil Compaction in Coffee Crops Based on LiDAR and RGB Sensors Carried by Remotely Piloted Aircraft
by Nicole Lopes Bento, Gabriel Araújo e Silva Ferraz, Lucas Santos Santana, Rafael de Oliveira Faria, Giuseppe Rossi and Gianluca Bambi
Remote Sens. 2025, 17(8), 1445; https://doi.org/10.3390/rs17081445 - 17 Apr 2025
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Abstract
Remotely Piloted Aircraft (RPA) as sensor-carrying airborne platforms for indirect measurement of plant physical parameters has been discussed in the scientific community. The utilization of RGB sensors with photogrammetric data processing based on Structure-from-Motion (SfM) and Light Detection and Ranging (LiDAR) sensors for [...] Read more.
Remotely Piloted Aircraft (RPA) as sensor-carrying airborne platforms for indirect measurement of plant physical parameters has been discussed in the scientific community. The utilization of RGB sensors with photogrammetric data processing based on Structure-from-Motion (SfM) and Light Detection and Ranging (LiDAR) sensors for point cloud construction are applicable in this context and can yield high-quality results. In this sense, this study aimed to compare coffee plant height data obtained from RGB/SfM and LiDAR point clouds and to estimate soil compaction through penetration resistance in a coffee plantation located in Minas Gerais, Brazil. A Matrice 300 RTK RPA equipped with a Zenmuse L1 sensor was used, with RGB data processed in PIX4D software (version 4.5.6) and LiDAR data in DJI Terra software (version V4.4.6). Canopy Height Model (CHM) analysis and cross-sectional profile, together with correlation and statistical difference studies between the height data from the two sensors, were conducted to evaluate the RGB sensor’s capability to estimate coffee plant height compared to LiDAR data considered as reference. Based on the height data obtained by the two sensors, soil compaction in the coffee plantation was estimated through soil penetration resistance. The results demonstrated that both sensors provided dense point clouds from which plant height (R2 = 0.72, R = 0.85, and RMSE = 0.44) and soil penetration resistance (R2 = 0.87, R = 0.8346, and RMSE = 0.14 m) were accurately estimated, with no statistically significant differences determined between the analyzed sensor data. It is concluded, therefore, that the use of remote sensing technologies can be employed for accurate estimation of coffee plantation heights and soil compaction, emphasizing a potential pathway for reducing laborious manual field measurements. Full article
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