The Role of UAVs in Modern Agriculture: Precision Spraying and Crop Health Analysis

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 2420

Special Issue Editors


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Guest Editor
Department of Mechanics and Construction, Faculty of Mechanical Engineering and Energy, Koszalin University of Technology, Śniadeckich Str. 2, 75-453 Koszalin, Poland
Interests: agricultural engineering; unmanned aerial vehicles; plant spraying; biological control agents; remote sensing; optical sensors; image analysis; vegetation indices; biomass energy

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Guest Editor
Department of Soil, Plant and Food Science, University of Bari Aldo Moro, Via Amendola 165/A, 70126 Bari, Italy
Interests: agricultural robotics; unmanned ground vehicles; unmanned aerial vehicles; remote sensing; sensors; agricultural automation; small unmanned aircraft systems (sUAS); agricultural robotics; machine-vision; renewable energies
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Geology, Soil Science and Geoinformation, Institute of Earth and Environmental Sciences, Faculty of Earth Sciences and Spatial Management, Maria Curri-Skłodowska University in Lublin, 5 M. Curie-Skłodowskiej Square, 20-031 Lublin, Poland
Interests: remote sensing; vegetation indices; UAVs

Special Issue Information

Dear Colleagues,

Unmanned aerial vehicles (UAVs) are playing an increasingly important role in modern agriculture, particularly in plant health assessment, pest detection, and precision spraying. As such drones are not impeded by physical barriers—they can move in any direction above plants and at varying heights, and can reach places that cannot be reached by ground equipment. Thanks to advanced sensors and artificial intelligence, drones can quickly and accurately monitor the condition of crops over large areas, identifying plants at risk of disease or pest attack. Mapping the field and identifying areas of change enables farmers to take preventive or corrective action at the most appropriate time. In terms of spraying, drones make it possible to apply chemicals precisely to the plants that require protection, which significantly reduces treatment time and the amount of pesticide used, thus minimizing negative impacts on the environment. The automation of this process reduces costs and increases the effectiveness of protective measures. The role of drones in agriculture is expected to continue growing, contributing to higher yields, improved environmental protection, and sustainable development.

This Special Issue aims to gather together original scientific articles and review articles that provide insights into the use of unmanned aerial vehicles for the identification and precise control of crop pests.

This Special Issue will welcome manuscripts that link the following themes:

  • The effects of using various optical cameras and other sensors on the identification of plant pests;
  • The use of drones for mapping fields and habitats of weeds, pests, and diseases in crops—methods, and approaches;
  • The design, equipment, precision, and parameters of unmanned aerial vehicles used for crop spraying;
  • Analysis of liquid application to plants and the biological effectiveness of plant spraying performed by drones;
  • Artificial intelligence and automation in plant pest identification and spraying processes;
  • Numerical simulations in the analysis of physical phenomena occurring during plant spraying from drones.

We look forward to receiving your original research articles and reviews.

Prof. Dr. Jerzy Chojnacki
Dr. Simone Pascuzzi
Dr. Marcin Siłuch
Guest Editors

Manuscript Submission Information

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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

  • drone crop monitoring
  • field mapping
  • vegetation indexes
  • imaging and sensor technologies
  • artificial intelligence
  • aerial spraying
  • remote agriculture
  • digital simulations

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Published Papers (3 papers)

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Research

15 pages, 1851 KB  
Article
First Attempts to Control Forest Pests Using Multi-Rotor Unmanned Aerial Spraying Systems (UASSs) in Forest Ecosystems
by Marius Paraschiv, Andrei Buzatu, Cosmin Paraschivoiu and Dănuț Chira
Drones 2026, 10(3), 181; https://doi.org/10.3390/drones10030181 - 6 Mar 2026
Viewed by 108
Abstract
Large-scale forest pest management has traditionally relied on aerial spraying; however, increasing regulatory restrictions and environmental concerns have limited its application in many regions. Unmanned Aerial Spraying System (UASS) platforms for aerial spraying have developed intensively in the last decade for pesticide application [...] Read more.
Large-scale forest pest management has traditionally relied on aerial spraying; however, increasing regulatory restrictions and environmental concerns have limited its application in many regions. Unmanned Aerial Spraying System (UASS) platforms for aerial spraying have developed intensively in the last decade for pesticide application in agricultural crops but remain scarcely explored within the forestry sector. This study evaluates the feasibility of UASS-based spraying platforms for forest pest control. We tested a multi-rotor agricultural UASS in two different forest conditions: broadleaf and conifer stands. Both biological and synthetic insecticides were sprayed against two contrasting forest pests, Lymantria dispar and Adelges laricis. Defoliation and infestation intensity were used to assess treatment efficacy post-application. Results indicated differences in operational productivity between forest stand types, with higher treatment efficacy observed for L. dispar. Despite the correct dosage delivered by the UASS, the target organism showed a limited biological response in the conifer pest. In conclusion the use of UASSs in forest ecosystems is conditioned by forest-specific factors; however, these technologies show potential to be aligned with interventions targeting early-stage pest outbreaks. Full article
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16 pages, 4676 KB  
Article
Comparative Assessment of the Efficacy of Drone Spraying and Gun Spraying for Nano-Urea Application in a Maize Crop
by Ramesh Kumar Sahni, Satya Prakash Kumar, Deepak Thorat, Rajeshwar Sanodiya, Sapna Soni, Chetan Yumnam and Ved Prakash Chaudhary
Drones 2026, 10(1), 1; https://doi.org/10.3390/drones10010001 - 19 Dec 2025
Viewed by 1352
Abstract
Conventional methods of nano-urea application in maize cultivation, such as tractor-operated gun sprayers, involve high water usage, labor intensity, and operator health risks due to chemical exposure. The drone spraying system ensures precise and automated application of nano-urea with minimal resource use, labor [...] Read more.
Conventional methods of nano-urea application in maize cultivation, such as tractor-operated gun sprayers, involve high water usage, labor intensity, and operator health risks due to chemical exposure. The drone spraying system ensures precise and automated application of nano-urea with minimal resource use, labor requirement, and operator intervention. However, the efficacy of the drone spraying system for nano-urea application was not evaluated and compared with traditional spraying systems in field conditions. There is a need to evaluate whether drone-based spraying systems can provide an equally effective and more resource-efficient alternative to conventional spraying techniques. Therefore, this study evaluated the agronomic efficacy of a drone-based spraying platform in comparison to conventional tractor-operated gun sprayers for the foliar spray application of nano-urea in the maize crop. Field experiments were conducted during the 2024 Kharif season to evaluate changes in SPAD, NDVI values, and grain yield due to two spray application methods. Both spraying methods showed statistically similar NDVI and SPAD values eight days after nano-urea application, indicating comparable effectiveness in nutrient delivery. Maize yield was also observed to be statistically indistinguishable between the two methods (t (8) = 0.025503, p = 0.9803), with 2912 ± 375 kg/ha (mean ± SE) for the gun sprayer and 2928 ± 503 kg/ha for the drone sprayer treatments. However, the drone system demonstrated significant operational advantages, including 95% water savings and decreased operational time. These findings support the use of drone spraying as a sustainable, safe, and scalable alternative to traditional fertilization application practices in precision agriculture. Full article
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26 pages, 32319 KB  
Article
UAV LiDAR-Based Automated Detection of Maize Lodging in Complex Agroecosystems
by Yajin Wang, Fengbao Yang and Linna Ji
Drones 2025, 9(12), 876; https://doi.org/10.3390/drones9120876 - 18 Dec 2025
Viewed by 419
Abstract
Maize lodging poses a significant challenge to agricultural production, severely constraining yield improvement and mechanized harvesting efficiency. Under modern agricultural practices characterized by high-density planting and multi-variety intercropping, there is an urgent need for precise and efficient monitoring technologies to address lodging issues. [...] Read more.
Maize lodging poses a significant challenge to agricultural production, severely constraining yield improvement and mechanized harvesting efficiency. Under modern agricultural practices characterized by high-density planting and multi-variety intercropping, there is an urgent need for precise and efficient monitoring technologies to address lodging issues. This study utilized unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) to acquire high-precision point cloud data of field maize at full maturity. An innovative method was proposed to automatically identify structural differences induced by lodging by analyzing canopy structural similarity across multiple height thresholds through point cloud stratification. This approach enables automated monitoring of maize lodging in complex field environments. The experimental results demonstrate the following: (1) High-precision point cloud data effectively capture canopy structural differences caused by lodging. Based on the structural similarity change curve, the height threshold for lodging can be automatically identified (optimal threshold: 1.76 m), with a deviation of only 2.3% between the calculated lodging area and the manually measured reference (ground truth). (2) Sensitivity analysis of the height threshold shows that when the threshold fluctuates within a ±5 cm range (1.71–1.81 m), the calculation deviation of the lodging area remains below 10% (maximum deviation = 8.2%), indicating strong robustness of the automatically selected threshold. (3) Although UAV flight altitude influences point cloud quality (e.g., low altitude: 25 m, high altitude: 80 m), the height threshold derived from low-altitude flights can be extrapolated to high-altitude monitoring to some extent. In this study, the resulting deviation in lodging area calculation was only 5.3%. Full article
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