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Agronomy
Special Issues
New Trends in Agricultural UAV Application—2nd Edition
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New Trends in Agricultural UAV Application—2nd Edition

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 2439

Special Issue Editors

Dr. Yali Zhang

E-Mail Website
Guest Editor
1. College of Engineering, South China Agricultural University, Wushan Road, Guangzhou 510642, China
2. National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China
Interests: UAV; precision spraying; sensors and controls
Special Issues, Collections and Topics in MDPI journals
Dr. Pengchao Chen

E-Mail Website
Guest Editor
1. College of Electronic Engineering and Artificial Intelligence, South China Agricultural University, Wushan Road, Guangzhou 510642, China
2. National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China
Interests: unmanned aerial spraying system (UASS); pesticide application technology; remote sensing; prescription map; spray drift
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

UAVs have demonstrated significant advantages in agricultural scenarios such as crop information monitoring, disease and pest detection, and the aerial application of , etc. For example, UAV sprayers have a proven ability in spraying and work efficiency in field crops such as rice, wheat, and corn. Still, their application in areas such as steep mountain slopes and densely planted orchards needs further exploration and improvement. At the same time, the environmental drift caused by spraying also deserves our attention. When considering precision spraying strategies, we must include the crop canopy characteristics, as well as disease levels based on remote sensing, in the scope of variable spraying decisions. When examining the different agricultural production problems, we use the UAV platform to generate new solutions, model methods, and control strategies, which will the focus of this Issue.

The theme of this Special Issue is “New Trends in Agricultural UAV Application”. We encourage the exploration and application research on UAVs from various fields of agriculture in different areas, including, but not limited to, agricultural remote sensing, pesticide spraying, and mechanical system structure innovation, covering remote sensing, plant science, agronomy, and engineering technology.

All manuscript types, such as original research papers and reviews, are welcome.

Dr. Yali Zhang
Dr. Pengchao Chen
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. Agronomy 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 (UAVs)
  • remote sensing
  • sustainable agriculture
  • variable rate application technology
  • artificial intelligence (AI)
  • deep learning (DL)
  • agricultural information acquisition
  • 3D reconstruction
  • plant phenotyping
  • Internet of Things (IoT)

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Related Special Issue

Published Papers (5 papers)

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Research

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30 pages, 10008 KiB  
Article
Integrating Stride Attention and Cross-Modality Fusion for UAV-Based Detection of Drought, Pest, and Disease Stress in Croplands
by Yan Li, Yaze Wu, Wuxiong Wang, Huiyu Jin, Xiaohan Wu, Jinyuan Liu, Chen Hu and Chunli Lv
Agronomy 2025, 15(5), 1199; https://doi.org/10.3390/agronomy15051199 - 15 May 2025
Viewed by 94
Abstract
Timely and accurate detection of agricultural disasters is crucial for ensuring food security and enhancing post-disaster response efficiency. This paper proposes a deployable UAV-based multimodal agricultural disaster detection framework that integrates multispectral and RGB imagery to simultaneously capture the spectral responses and spatial [...] Read more.
Timely and accurate detection of agricultural disasters is crucial for ensuring food security and enhancing post-disaster response efficiency. This paper proposes a deployable UAV-based multimodal agricultural disaster detection framework that integrates multispectral and RGB imagery to simultaneously capture the spectral responses and spatial structural features of affected crop regions. To this end, we design an innovative stride–cross-attention mechanism, in which stride attention is utilized for efficient spatial feature extraction, while cross-attention facilitates semantic fusion between heterogeneous modalities. The experimental data were collected from representative wheat and maize fields in Inner Mongolia, using UAVs equipped with synchronized multispectral (red, green, blue, red edge, near-infrared) and high-resolution RGB sensors. Through a combination of image preprocessing, geometric correction, and various augmentation strategies (e.g., MixUp, CutMix, GridMask, RandAugment), the quality and diversity of the training samples were significantly enhanced. The model trained on the constructed dataset achieved an accuracy of 93.2%, an F1 score of 92.7%, a precision of 93.5%, and a recall of 92.4%, substantially outperforming mainstream models such as ResNet50, EfficientNet-B0, and ViT across multiple evaluation metrics. Ablation studies further validated the critical role of the stride attention and cross-attention modules in performance improvement. This study demonstrates that the integration of lightweight attention mechanisms with multimodal UAV remote sensing imagery enables efficient, accurate, and scalable agricultural disaster detection under complex field conditions. Full article
(This article belongs to the Special Issue New Trends in Agricultural UAV Application—2nd Edition)
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23 pages, 4583 KiB  
Article
A Reinforcement Learning-Driven UAV-Based Smart Agriculture System for Extreme Weather Prediction
by Jiarui Hao, Bo Li, Weidong Tang, Shiya Liu, Yihe Chang, Jianxiang Pan, Yang Tao and Chunli Lv
Agronomy 2025, 15(4), 964; https://doi.org/10.3390/agronomy15040964 - 16 Apr 2025
Viewed by 396
Abstract
Extreme weather prediction plays a crucial role in agricultural production and disaster prevention. This study proposes a lightweight extreme weather early warning model based on UAV cruise monitoring, a density-aware attention mechanism, and edge computing. Reinforcement learning is utilized to optimize UAV cruise [...] Read more.
Extreme weather prediction plays a crucial role in agricultural production and disaster prevention. This study proposes a lightweight extreme weather early warning model based on UAV cruise monitoring, a density-aware attention mechanism, and edge computing. Reinforcement learning is utilized to optimize UAV cruise paths, while a Transformer-based model is employed for weather prediction. Experimental results demonstrate that the proposed method achieves an overall prediction accuracy of 0.91, a precision of 0.93, a recall of 0.88, and an F1-score of 0.91. In the prediction of different extreme weather events, the proposed method attains an accuracy of 0.89 for strong wind conditions, 0.92 for hail, and 0.89 for late spring cold, all outperforming state-of-the-art methods. These results validate the effectiveness and applicability of the proposed approach in extreme weather forecasting. Full article
(This article belongs to the Special Issue New Trends in Agricultural UAV Application—2nd Edition)
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22 pages, 3547 KiB  
Article
Classification of Garden Chrysanthemum Flowering Period Using Digital Imagery from Unmanned Aerial Vehicle (UAV)
by Jiuyuan Zhang, Jingshan Lu, Qimo Qi, Mingxiu Sun, Gangjun Zheng, Qiuyan Zhang, Fadi Chen, Sumei Chen, Fei Zhang, Weimin Fang and Zhiyong Guan
Agronomy 2025, 15(2), 421; https://doi.org/10.3390/agronomy15020421 - 7 Feb 2025
Viewed by 781
Abstract
Monitoring the flowering period is essential for evaluating garden chrysanthemum cultivars and their landscaping use. However, traditional field observation methods are labor-intensive. This study proposes a classification method based on color information from canopy digital images. In this study, an unmanned aerial vehicle [...] Read more.
Monitoring the flowering period is essential for evaluating garden chrysanthemum cultivars and their landscaping use. However, traditional field observation methods are labor-intensive. This study proposes a classification method based on color information from canopy digital images. In this study, an unmanned aerial vehicle (UAV) with a red-green-blue (RGB) sensor was utilized to capture orthophotos of garden chrysanthemums. A mask region-convolutional neural network (Mask R-CNN) was employed to remove field backgrounds and categorize growth stages into vegetative, bud, and flowering periods. Images were then converted to the hue-saturation-value (HSV) color space to calculate eight color indices: R_ratio, Y_ratio, G_ratio, Pink_ratio, Purple_ratio, W_ratio, D_ratio, and Fsum_ratio, representing various color proportions. A color ratio decision tree and random forest model were developed to further subdivide the flowering period into initial, peak, and late periods. The results showed that the random forest model performed better with F1-scores of 0.9040 and 0.8697 on two validation datasets, requiring less manual involvement. This method provides a rapid and detailed assessment of flowering periods, aiding in the evaluation of new chrysanthemum cultivars. Full article
(This article belongs to the Special Issue New Trends in Agricultural UAV Application—2nd Edition)
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17 pages, 6470 KiB  
Article
Optimization of Flight Mode and Coupling Analysis of Operational Parameters on Droplet Deposition and Drift of Unmanned Aerial Spraying Systems (UASS)
by Qi Liu, Ding Ma, Haiyan Zhang, Long Wu, Long Zhang, Huifang Bao and Yubin Lan
Agronomy 2025, 15(2), 367; https://doi.org/10.3390/agronomy15020367 - 30 Jan 2025
Viewed by 642
Abstract
In recent years, extensive research has been conducted on pesticide application technology using unmanned aerial spraying systems (UASS) due to their efficiency and ability to overcome terrain obstacles. However, the coupling effect between the operational parameters of UASS and their influence on droplet [...] Read more.
In recent years, extensive research has been conducted on pesticide application technology using unmanned aerial spraying systems (UASS) due to their efficiency and ability to overcome terrain obstacles. However, the coupling effect between the operational parameters of UASS and their influence on droplet deposition has not been sufficiently studied. A thorough and methodical analysis is essential to assess the deposition performance and drift risk of UASS. This study evaluated the spraying performance of an electric six-rotor UASS in wheat fields in Zibo between 2021 and 2022, focusing on three operational modes determined by flight speed and flow rate. Furthermore, the individual effects of these two parameters on droplet deposition quality and drift risk were explored. Based on the deposition quality of in-swath droplets and the drift degree after application, the results demonstrate that the optimal comprehensive characteristics of droplet deposition occur at a flight speed of 4.5 m/s, a flow rate of 2.025 L/min, and a spray amount of 1 L/ha. The increase in spray flow rate (2.475 L/min) results in a 3.92-fold enhancement in the deposition rate within the spray area compared with that of group of the minimum spray flow rate (1.575 L/min). A higher flight speed (5.5 m/s) improves the uniformity of droplet deposition, with the coefficient of variation decreases by 25.2% compared with that of the minimum flight speed (3.5 m/s), and this higher flight speed leads to a drift distance of 28.8 m. Full article
(This article belongs to the Special Issue New Trends in Agricultural UAV Application—2nd Edition)
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Review

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21 pages, 6121 KiB  
Review
Review of Active Plant Frost Protection Equipment and Technologies: Current Status, Challenges, and Future Prospects
by Tianhong Liu, Songchao Zhang, Tao Sun, Cong Ma and Xinyu Xue
Agronomy 2025, 15(5), 1164; https://doi.org/10.3390/agronomy15051164 - 10 May 2025
Viewed by 240
Abstract
Frost poses a significant threat to agricultural production, leading to reduced crop yields and deterioration in quality. This review systematically provides an overview of the types and causes of plant frost, and delves into the principles, research progress, and application status of three [...] Read more.
Frost poses a significant threat to agricultural production, leading to reduced crop yields and deterioration in quality. This review systematically provides an overview of the types and causes of plant frost, and delves into the principles, research progress, and application status of three key active frost protection (FP) technologies: air disturbance, sprinkler irrigation, and heating. It also scrutinizes the challenges faced by current FP equipment, such as high costs, complex maintenance, and noise pollution. Air disturbance technology utilizes fans to mix upper and lower air layers, increasing the canopy temperature, with research focusing on fan optimization and unmanned aerial vehicle (UAV) application. Sprinkler irrigation technology releases latent heat through water freezing, with research centering on water saving and automation. Heating technology directly supplies heat, with attention on heat source optimization and mobile heating strategies. Finally, this review outlines the development trends of plant FP equipment and technologies, highlighting the promising application prospects of agricultural UAVs in FP, which can have multi-purpose use and effectively reduce costs. Full article
(This article belongs to the Special Issue New Trends in Agricultural UAV Application—2nd Edition)
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