Agronomy

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21 pages, 5145 KB

Open AccessArticle

Synchronous Spray Effect Based on Dual Plant-Protection UAV Collaboration in Corn Fields

by Shenghui Yang, Shuyuan Zhai, Xiangye Yu, Weihong Liu, Yongjun Zheng, Hangxing Zhao, Han Feng, Haoyu Wang and Wenbo Xu

Agronomy 2026, 16(3), 292; https://doi.org/10.3390/agronomy16030292 - 24 Jan 2026

Viewed by 368

Abstract

It has become common to apply multiple drones to conduct plant-protection in large-scale farms, where dual-UAV synchronisation is representative. However, current studies are mainly dedicated to the spray quality of a single UAV, and it remains unclear whether synchronous operation affects spray effectiveness. [...] Read more.

It has become common to apply multiple drones to conduct plant-protection in large-scale farms, where dual-UAV synchronisation is representative. However, current studies are mainly dedicated to the spray quality of a single UAV, and it remains unclear whether synchronous operation affects spray effectiveness. This paper focuses on the spray efficacy and coupling effects of dual-UAV collaboration. Five-factor orthogonal four-level tests were conducted using the developed UAV collaboration system, and the results were compared with those of asynchronous and ideal linear superposition. It is indicated that (1) spray uniformity was impacted by the relative height between the UAVs and the flight speed of the UAVs (all the p-values < 0.02), whilst the deposition amount was affected by the relative horizontal spacing between the UAVs and the height of the left UAV relative to the forward flight direction (all the p-values < 0.04); (2) the proportion of high-quality spray in the coupling areas had a negative relation with the relative horizontal distance of the two UAVs, and the threshold of the effective coupling distance was 5 m; and (3) synchronous coupling should be avoided. If it is not, the left-side UAV (referring to the forward direction of flight) should be at a higher altitude (5 m or 6.5 m), be 0.5 m higher than the right and fly with a low or medium flight speed (3.5 m/s–4.5 m/s). The research can give a reference to the real spray operation by multiple UAVs. Full article

(This article belongs to the Special Issue New Trends in Agricultural UAV Application—2nd Edition)

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30 pages, 10008 KB

Open AccessArticle

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

Cited by 10 | Viewed by 1930

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 KB

Open AccessArticle

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

Cited by 4 | Viewed by 2013

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 KB

Open AccessArticle

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

Cited by 3 | Viewed by 2170

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 KB

Open AccessArticle

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

Cited by 5 | Viewed by 1643

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

Jump to: Research

39 pages, 1243 KB

Open AccessEditor’s ChoiceReview

From Sensing to Intervention: A Critical Review of Agricultural Drones for Precision Agriculture, Data-Driven Decision Making, and Sustainable Intensification

by Vlad Nicolae Arsenoaia, Denis Constantin Topa, Roxana Nicoleta Ratu and Ioan Tenu

Agronomy 2026, 16(5), 564; https://doi.org/10.3390/agronomy16050564 - 4 Mar 2026

Viewed by 1082

Abstract

Unmanned aerial vehicles (UAVs) are increasingly employed in precision agronomy to support high-resolution monitoring and management of crops; however, the extent to which UAV-derived data can be translated into reliable, scalable, and decision-ready applications remains inconsistent. This review addresses this gap by critically [...] Read more.

Unmanned aerial vehicles (UAVs) are increasingly employed in precision agronomy to support high-resolution monitoring and management of crops; however, the extent to which UAV-derived data can be translated into reliable, scalable, and decision-ready applications remains inconsistent. This review addresses this gap by critically synthesising the recent literature with a specific focus on the end-to-end data pipeline, from acquisition planning and pre-processing to data fusion, analytics readiness, and operational decision support. A systematic analysis of peer-reviewed studies published over the last five years was conducted to evaluate core agronomic applications, including crop health monitoring, precision irrigation, soil and field variability assessment, spraying, and yield prediction, with particular attention to indicators used, validation strategies, and reported agronomic outcomes. The findings indicate that monitoring and diagnostic applications are the most mature and consistently validated, whereas interventional uses and absolute yield prediction remain strongly context-dependent and constrained by operational, methodological, and regulatory factors. Across applications, pipeline robustness, uncertainty management, and reproducibility emerge as more critical determinants of agronomic value than sensor resolution alone. The review further identifies key barriers to scaling, including technical limitations, skills requirements, data integration challenges, and regulatory constraints, and outlines an innovation roadmap distinguishing currently deployable solutions from emerging developments over the next three to five years. Overall, this work provides a decision-oriented framework to support more transparent, validated, and sustainable integration of UAV technologies into modern agricultural systems. Full article

(This article belongs to the Special Issue New Trends in Agricultural UAV Application—2nd Edition)

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37 pages, 1846 KB

Open AccessEditor’s ChoiceReview

Visualization Techniques for Spray Monitoring in Unmanned Aerial Spraying Systems: A Review

by Jungang Ma, Hua Zhuo, Peng Wang, Pengchao Chen, Xiang Li, Mei Tao and Zongyin Cui

Agronomy 2026, 16(1), 123; https://doi.org/10.3390/agronomy16010123 - 4 Jan 2026

Cited by 2 | Viewed by 1112

Abstract

Unmanned Aerial Spraying Systems (UASS) has rapidly advanced precision crop protection. However, the spray performance of UASSs is influenced by nozzle atomization, rotor-induced airflow, and external environmental conditions. These factors cause strong spatiotemporal coupling and high uncertainty. As a result, visualization-based monitoring techniques [...] Read more.

Unmanned Aerial Spraying Systems (UASS) has rapidly advanced precision crop protection. However, the spray performance of UASSs is influenced by nozzle atomization, rotor-induced airflow, and external environmental conditions. These factors cause strong spatiotemporal coupling and high uncertainty. As a result, visualization-based monitoring techniques are now essential for understanding these dynamics and supporting spray modeling and drift-mitigation design. This review highlights developments in spray visualization technologies along the “droplet–airflow–target” chain mechanism in UASS spraying. We first outline the physical fundamentals of droplet formation, liquid-sheet breakup, droplet size distribution, and transport mechanisms in rotor-induced flow. Dominant processes are identified across near-field, mid-field, and far-field scales. Next, we summarize major visualization methods. These include optical imaging (PDPA/PDIA, HSI, DIH), laser-based scattering and ranging (LD, LiDAR), and flow-field visualization (PIV). We compare their spatial resolution, measurement range, 3D reconstruction capabilities, and possible sources of error. We then review wind-tunnel trials, field experiments, and point-cloud reconstruction studies. These studies show how downwash flow and tip vortices affect plume structure, canopy disturbance, and deposition patterns. Finally, we discuss emerging intelligent analysis for large-scale monitoring—such as image-based droplet recognition, multimodal data fusion, and data-driven modeling. We outline future directions, including unified feature systems, vortex-coupled models, and embedded closed-loop spray control. This review is a comprehensive reference for advancing UASS analysis, drift assessment, spray optimization, and smart support systems. Full article

(This article belongs to the Special Issue New Trends in Agricultural UAV Application—2nd Edition)

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21 pages, 6121 KB

Open AccessReview

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

Cited by 6 | Viewed by 4170

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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