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Advances of UAV in Precision Agriculture—2nd Edition
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Advances of UAV in Precision Agriculture—2nd Edition

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

Deadline for manuscript submissions: 25 September 2025 | Viewed by 1481

Special Issue Editors

Prof. Dr. Jian Chen

E-Mail Website
Guest Editor
College of Engineering, China Agricultural University, Beijing 100083, China
Interests: bio-inspired perception and embodied intelligent control of unmanned systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yangquan Chen

grade E-Mail Website
Guest Editor
Department of Mechanical Engineering (ME), University of California, Merced, CA 95343, USA
Interests: mechatronics for sustainability; cognitive process control (smart control engineering via digital twins); small multi-UAV-based cooperative multi-spectral “personal remote sensing”; applied fractional calculus in controls, modeling, and complex signal processing; distributed measurements; control of distributed parameter systems with mobile actuators and sensor networks
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yanbo Huang

E-Mail Website
Guest Editor
Genetics and Sustainable Agriculture Research Unit, U.S. Department of Agriculture–Agricultural Research Service, Mississippi State, MS 39762, USA
Interests: process modeling; optimization and control; remote sensing; artificial intelligence; machine learning; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dongbing Gu

E-Mail Website
Guest Editor
School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, Essex, UK
Interests: advanced robotic technologies; frontiers in robotics and AI: multi-robot systems

Special Issue Information

Dear Colleagues,

At present, intelligent agricultural unmanned systems cover four spatial dimensions with broad development prospects: space (navigation, remote sensing, meteorological, and communication satellites); air (plant protection UAVs, remote sensing and mapping UAVs, long-endurance solar-powered UAVs, long-endurance airships, and bionic flying robots); ground (unmanned farming/harvesting machinery, biomass energy systems, soil-improving bionic robots, and unmanned animal husbandry robots); and water (unmanned underwater vehicles, underwater operation robots, and unmanned aquaculture systems). Establishing an agricultural integrated space–air–ground–water cooperation and precision operation system based on the closed-loop control of large systems, studying the intelligent sensing and control technology of intelligent agricultural unmanned systems and establishing application demonstration bases all over the world play an important role in supporting huge developments regarding automotive operations, intelligent operations, unmanned operations, and cluster operations of intelligent agricultural machinery and equipment. It is also of great significance to realize the short-term goal “unmanned farming” and the long-term goal “unmanned agriculture” of world agricultural modernization.

Continuing from the first edition “Advances of UAV in Precision Agriculture”, this Special Issue aims to publish state-of-the-art advances and the latest achievements of UAV technologies in precision agriculture that fully relate to the journal’s scope.

We welcome articles covering recent research on various topics including, but not limited to, the following:

  • Agricultural information-integrated space–air–ground–water remote sensing and monitoring networks (satellites, UAVs, UGVs, USVs and UUVs) and multi-source data fusion for agricultural applications;
  • Unmanned agricultural intelligent sensing and control systems, intelligent agricultural equipment, and autonomous systems for agricultural machinery field operations;
  • Unmanned simultaneous localization and mapping and the sensing of unmanned robots in agriculture;
  • Unmanned agricultural robot guidance (path planning), navigation and control;
  • Bio-inspired swarm intelligence and multi-agent system cooperative control;
  • Unmanned soil moisture and crop phenotype detection, hyper-spectral sensing, and quantitative inversion;
  • Spray or seeding drones for agricultural applications (fertilization and crop protection);
  • Drones for precision agriculture, e.g., nutrients analysis, insect infestation analysis, fungus infestation analysis, snail attack mapping, soil quality and soil compaction mapping, drainage system analysis, harvest prediction;
  • Bionic flying robots and flying robots with a soft grasping manipulator;
  • Drones in/for greenhouses.

Prof. Dr. Jian Chen
Prof. Dr. Yangquan Chen
Prof. Dr. Yanbo Huang
Prof. Dr. Dongbing Gu
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. 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

  • agricultural UAV
  • guidance, navigation and control
  • SLAM
  • swarm intelligence
  • remote sensing
  • crop phenotype awareness
  • crop and/or water stress assessment
  • drones for agricultural applications
  • drones for precision agriculture
  • precision viticulture

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (3 papers)

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Research

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17 pages, 2085 KiB  
Article
Agricultural Drone-Based Variable-Rate N Application for Regulating Wheat Protein Content
by Senlin Guan, Yumi Shimazaki, Kimiyasu Takahashi, Hitoshi Kato, Koichiro Fukami and Shuichi Watanabe
Drones 2025, 9(4), 310; https://doi.org/10.3390/drones9040310 - 16 Apr 2025
Viewed by 278
Abstract
Implementing a variable-rate application (VRA) of fertilization based on real-time crop growth status reduces costs and enhances work efficiency. However, the technical challenges associated with obtaining accurate growth-distribution maps and applying VRA, particularly with agricultural drones, remain underexplored. In this study, we specifically [...] Read more.
Implementing a variable-rate application (VRA) of fertilization based on real-time crop growth status reduces costs and enhances work efficiency. However, the technical challenges associated with obtaining accurate growth-distribution maps and applying VRA, particularly with agricultural drones, remain underexplored. In this study, we specifically focused on agricultural drone-based VRA fertilization for regulating wheat protein content. First, normalized difference vegetation index (NDVI) distribution maps were obtained using multispectral images captured using a small unmanned aerial vehicle. Subsequently, a prescription map based on the NDVI values was generated to facilitate the implementation of VRA for fertilization. Continuous monitoring of changes in related vegetation indices was conducted from post-topdressing to harvest. Experimental results indicated that selecting targeted experimental survey areas based on different growth conditions can result in accurate predictions of the final yield. However, it is sill ineffective for predicting protein content or protein yield. Additionally, VRA fertilization with less fertilizer in high-NDVI areas and more fertilizer in low-NDVI areas showed no significant difference in final protein content or protein yield compared to conventional uniform fertilization. These findings provide reference data for advancing precision agriculture by addressing field-scale variability for high-quality and uniform production while presenting further research challenges. Full article
(This article belongs to the Special Issue Advances of UAV in Precision Agriculture—2nd Edition)
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19 pages, 12418 KiB  
Article
Integration of UAV Multi-Source Data for Accurate Plant Height and SPAD Estimation in Peanut
by Ning He, Bo Chen, Xianju Lu, Bo Bai, Jiangchuan Fan, Yongjiang Zhang, Guowei Li and Xinyu Guo
Drones 2025, 9(4), 284; https://doi.org/10.3390/drones9040284 - 8 Apr 2025
Viewed by 191
Abstract
Plant height and SPAD values are critical indicators for evaluating peanut morphological development, photosynthetic efficiency, and yield optimization. Recent unmanned aerial vehicle (UAV) technology advancements have enabled high-throughput phenotyping at field scales. As a globally strategic oilseed crop, peanut plays a vital role [...] Read more.
Plant height and SPAD values are critical indicators for evaluating peanut morphological development, photosynthetic efficiency, and yield optimization. Recent unmanned aerial vehicle (UAV) technology advancements have enabled high-throughput phenotyping at field scales. As a globally strategic oilseed crop, peanut plays a vital role in ensuring food and edible oil security. This study aimed to develop an optimized estimation framework for peanut plant height and SPAD values through machine learning-driven integration of UAV multi-source data while evaluating model generalizability across temporal and spatial domains. Multispectral UAV and ground data were collected across four growth stages (2023–2024). Using spectral indices and Texture features, four models (PLSR, SVM, ANN, RFR) were trained on 2024 data and independently validated with 2023 datasets. The ensemble machine learning models (RFR) significantly enhanced estimation accuracy (R2 improvement: 3.1–34.5%) and robustness compared to the linear model (PLSR). Feature stability analysis revealed that combined spectral-textural features outperformed single-feature approaches. The SVM model achieved superior plant height prediction (R2 = 0.912, RMSE = 2.14 cm), while RFR optimally estimated SPAD values (R2 = 0.530, RMSE = 3.87) across heterogeneous field conditions. This UAV-based multi-modal integration framework demonstrates significant potential for temporal monitoring of peanut growth dynamics. Full article
(This article belongs to the Special Issue Advances of UAV in Precision Agriculture—2nd Edition)
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Review

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25 pages, 1980 KiB  
Review
UAV-Based Soil Water Erosion Monitoring: Current Status and Trends
by Beatriz Macêdo Medeiros, Bernardo Cândido, Paul Andres Jimenez Jimenez, Junior Cesar Avanzi and Marx Leandro Naves Silva
Drones 2025, 9(4), 305; https://doi.org/10.3390/drones9040305 - 14 Apr 2025
Viewed by 492
Abstract
Soil erosion affects land productivity, water quality, and ecosystem resilience. Traditional monitoring methods are often time-consuming, labor-intensive, and resource-demanding, while unmanned aerial vehicles (UAVs) provide high-resolution, near-real-time data, improving accuracy. This study conducts a bibliometric analysis of UAV-based soil erosion research to explore [...] Read more.
Soil erosion affects land productivity, water quality, and ecosystem resilience. Traditional monitoring methods are often time-consuming, labor-intensive, and resource-demanding, while unmanned aerial vehicles (UAVs) provide high-resolution, near-real-time data, improving accuracy. This study conducts a bibliometric analysis of UAV-based soil erosion research to explore trends, technologies, and challenges. A systematic review of Web of Science and Scopus articles identified 473 relevant studies after filtering for terms that refer to types of soil erosion. Analysis using R’s bibliometrix package shows research is concentrated in Asia, Europe, and the Americas, with 304 publications following a surge. Multi-rotor UAVs with RGB sensors are the most common. Gully erosion is the most studied form of the issue, followed by landslides, rills, and interrill and piping erosion. Significant gaps remain in rill and interrill erosion research. The integration of UAVs with satellite data, laser surveys, and soil properties is limited but crucial. While challenges such as data accuracy and integration persist, UAVs offer cost-effective, near-real-time monitoring capabilities, enabling rapid responses to erosion changes. Future work should focus on multi-source data fusion to enhance conservation strategies. Full article
(This article belongs to the Special Issue Advances of UAV in Precision Agriculture—2nd Edition)
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