Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

Applications of Unmanned Aerial Remote Sensing in Precision Agriculture

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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: 31 July 2026 | Viewed by 13685

Share This Special Issue

Special Issue Editors

Dr. Erica Isabella Parisi

E-Mail Website
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

Dr. Fabiana Di Ciaccio

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 250 words) can be sent to the Editorial Office for assessment.

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

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (10 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Other

26 pages, 2084 KB

Open AccessArticle

Phenology-Adaptive Prediction of Walnut Leaf Area Index from UAV Multispectral Data via Hybrid Feature Selection and SHAP-Enhanced Machine Learning

by Qiuhao Xia, Yerhazi Yerzati, Zihao Li, Jiahui Qi, Jiaxing Chen, Yu Sen, Rui Zhang, Yunqi Zhang, Hongxia Wang and Zhongzhong Guo

Remote Sens. 2026, 18(12), 1941; https://doi.org/10.3390/rs18121941 - 11 Jun 2026

Abstract

Accurate monitoring of the leaf area index (LAI) throughout the entire growth cycle of walnut trees using UAV multispectral imagery is essential for digital orchard management. In this study, focusing on the ‘Wen 185’ walnut variety in Xinjiang, we simultaneously acquired UAV multispectral images and ground-measured LAI data during four critical growth stages: expansion, hard shell, oil conversion, and maturity. A total of 25 vegetation indices and 48 texture features derived from the gray-level co-occurrence matrix were extracted. Hybrid feature selection combining linear (Pearson correlation), nonlinear (maximum information coefficient and random forest importance), and multiple consensus strategies was employed to reduce redundancy. LAI prediction models were constructed using four algorithms: Random Forest (RF), Support Vector Machine (SVM), LASSO, and Ridge Regression (RR), with model interpretability enhanced by SHAP analysis. Results showed that the multiple consensus screening reduced feature redundancy by an average of 69.6%. SHAP identified five core features: Redge_750_Mean, NDVI, B_Mean, RENDVI, and G_Homogeneity. Importantly, predictor importance shifted significantly with phenology: texture features dominated during the expansion stage, while red-edge indices (RENDVI and Redge_750_Mean) became predominant during the hard shell and oil conversion stages, effectively mitigating the saturation problem commonly observed in traditional indices such as NDVI within the LAI range of 1.5–5.8 in this study. The hybrid feature subset combining “red-edge spectrum + spatial texture” with the Random Forest algorithm achieved superior performance across all stages, with the RPD value exceeding 2.0 during the oil conversion stage, indicating excellent estimation capability. This study demonstrates that a “quality over quantity” feature selection strategy not only reduces model complexity but also enables high-precision, dynamic LAI monitoring throughout the entire walnut growth cycle, providing a scientific basis for intelligent management of large-scale orchards in arid regions. Full article

(This article belongs to the Special Issue Applications of Unmanned Aerial Remote Sensing in Precision Agriculture)

23 pages, 9496 KB

Open AccessArticle

Research on Walnut Yield Estimation Based on Interpretable Machine Learning and Stacked Integration Under Different Water–Fertilizer Coupling Regimes

by Yerhazi Yerzati, Qiuhao Xia, Langqin Luo, Jiaxing Chen, Jiahui Qi, Zhongzhong Guo, Changyuan Zhai, Yunqi Zhang and Rui Zhang

Remote Sens. 2026, 18(10), 1449; https://doi.org/10.3390/rs18101449 - 7 May 2026

Viewed by 379

Abstract

To overcome the limitations of traditional yield estimation methods—which are often subjective, costly, and difficult to implement at scale—this study developed a high-precision, interpretable model for predicting walnut yield by integrating multi-source remote sensing technology with interpretable machine learning. To provide a theoretical foundation for precise water and fertilizer management as well as intelligent production in walnut orchards. By employing interpretable machine learning and a multi-stage integration strategy, the model achieves not only high-precision yield estimation but also elucidates the influence pathways of water–fertilizer coupling on yield formation at a mechanistic level. This advancement offers reliable technical support and a decision-making framework for the precise management of orchards. This study focused on the Xinjiang ‘Wen 185’ walnut, employing field experiments with varying water and fertilizer gradients. A UAV equipped with a multispectral sensor was utilized to capture canopy images, from which vegetation indices and texture features were extracted. This process resulted in a comprehensive dataset that integrated remotely sensed features with management practices. Various machine learning algorithms, including random forest, support vector machine, partial least squares regression, and ridge regression, were applied. An innovative stacked integration model for growth stages was proposed, and the SHAP framework was incorporated to analyze feature contributions and enhance model interpretability. In this study, texture features—particularly those derived from the red-edge band—showed higher predictive importance than traditional vegetation indices. This suggests that they may be more sensitive to canopy structural heterogeneity under the tested conditions. Among the models, random forest showed numerically higher values in terms of R² and RPD compared to the other individual models under the present dataset, achieving a validation R² of 0.670 and an RPD of 1.836. The proposed growth stage stacking ensemble (GSSE) model further enhanced prediction accuracy, achieving validation R² of 0.789, an RMSE of 0.494, and an RPD of 2.296. Additionally, the results revealed that texture may have a potential ability to captured canopy heterogeneity as the primary mechanism underlying yield variation, and the integration of multi-stage spectral information was associated with higher estimation accuracy in this dataset in improving estimation accuracy, with the oil conversion stage contributing up to 60% to the final prediction. Full article

(This article belongs to the Special Issue Applications of Unmanned Aerial Remote Sensing in Precision Agriculture)

► Show Figures

Figure 1

23 pages, 14742 KB

Open AccessArticle

Grapevine Canopy Volume Estimation from UAV Photogrammetric Point Clouds at Different Flight Heights

by Leilson Ferreira, Pedro Marques, Emanuel Peres, Raul Morais, Joaquim J. Sousa and Luís Pádua

Remote Sens. 2026, 18(3), 409; https://doi.org/10.3390/rs18030409 - 26 Jan 2026

Cited by 1 | Viewed by 1107

Abstract

Vegetation volume is a useful indicator for assessing canopy structure and supporting vineyard management tasks such as foliar applications and canopy management. The photogrammetric processing of imagery acquired using unmanned aerial vehicles (UAVs) enables the generation of dense point clouds suitable for estimating canopy volume, although point cloud quality depends on spatial resolution, which is influenced by flight height. This study evaluates the effect of three flight heights (30 m, 60 m, and 100 m) on grapevine canopy volume estimation using convex hull, alpha shape, and voxel-based models. UAV-based RGB imagery and field measurements were collected during three periods at different phenological stages in an experimental vineyard. The strongest agreement with field-measured volume occurred at 30 m, where point density was highest. Envelope-based methods showed reduced performance at higher flight heights, while voxel-based grids remained more stable when voxel size was adapted to point density. Estimator behavior also varied with canopy architecture and development. The results indicate appropriate parameter choices for different flight heights and confirm that UAV-based RGB imagery can provide reliable grapevine canopy volume estimates. Full article

(This article belongs to the Special Issue Applications of Unmanned Aerial Remote Sensing in Precision Agriculture)

► Show Figures

Figure 1

27 pages, 2831 KB

Open AccessArticle

Effects of Flight and Processing Parameters on UAS Image-Based Point Clouds for Plant Height Estimation

by Chenghai Yang, Charles P.-C. Suh and Bradley K. Fritz

Remote Sens. 2026, 18(2), 360; https://doi.org/10.3390/rs18020360 - 21 Jan 2026

Viewed by 639

Abstract

Point clouds and digital surface models (DSMs) derived from unmanned aircraft system (UAS) imagery are widely used for plant height estimation in plant phenotyping and precision agriculture. However, comprehensive evaluations across multiple crops, flight altitudes, and image overlaps are limited, restricting guidance for optimizing flight strategies. This study evaluated the effects of flight altitude, side and front overlap, and image processing parameters on point cloud generation and plant height estimation. UAS imagery was collected at four altitudes (30–120 m, corresponding to 0.5–2.0 cm ground sampling distance, GSD) with multiple side and front overlaps (67–94%) over a 2–ha field planted with corn, cotton, sorghum, and soybean on three dates across two growing seasons, producing 90 datasets. Orthomosaics, point clouds, and DSMs were generated using Pix4Dmapper, and plant height estimates were extracted from both DSMs and point clouds. Results showed that point clouds consistently outperformed DSMs across altitudes, overlaps, and crop types. Highest accuracy occurred at 60–90 m (1.0–1.5 cm GSD) with RMSE values of 0.06–0.10 m (R² = 0.92–0.95) in 2019 and 0.07–0.08 m (R² = 0.80–0.89) in 2022. Across multiple side and front overlap combinations at 60–120 m, reduced overlaps produced RMSE values comparable to full overlaps, indicating that optimized flight settings, particularly reduced side overlap with high front overlap, can shorten flight and processing time without compromising point cloud quality or height estimation accuracy. Pix4Dmapper processing parameters strongly affected 3D point cloud density (2–600 million points), processing time (1–16 h), and plant height accuracy (R² = 0.67–0.95). These findings provide practical guidance for selecting UAS flight and processing parameters to achieve accurate, efficient 3D modeling and plant height estimation. By balancing flight altitude, image side and front overlap, and photogrammetric processing settings, users can improve operational efficiency while maintaining high-accuracy plant height measurements, supporting faster and more cost-effective phenotyping and precision agriculture applications. Full article

(This article belongs to the Special Issue Applications of Unmanned Aerial Remote Sensing in Precision Agriculture)

► Show Figures

Figure 1

20 pages, 5780 KB

Open AccessArticle

Effects of Spatial Resolution on Assessing Cotton Water Stress Using Unmanned Aerial System Imagery

by Oluwatola Adedeji, Yazhou Sun, Sanai Li and Wenxuan Guo

Remote Sens. 2025, 17(24), 4018; https://doi.org/10.3390/rs17244018 - 12 Dec 2025

Cited by 5 | Viewed by 856

Abstract

Accurate detection of cotton water stress is essential for improving irrigation efficiency and yield prediction. Unmanned aerial system (UAS) imagery offers an effective means for high-throughput crop monitoring, yet its performance across spatial resolutions remains insufficiently characterized. This study aimed to (1) evaluate the performance of UAS-derived Water Deficit Index (WDI) and Crop Water Stress Index (CWSI) across cotton growth stages and (2) examine how spatial resolution influences stress detection and yield prediction. Field experiments were conducted in Lubbock County, Texas, during the 2021–2022 growing seasons under three irrigation treatments (30%, 60%, and 90% ET replacement). Multispectral and thermal UAS imagery were processed to generate WDI and CWSI maps at spatial resolutions ranging from 0.1 to 4.0 m. Results showed that WDI outperformed CWSI at distinguishing water-stress levels, particularly during early growth stages. A 0.5 m resolution provided the best balance between detection accuracy and computational efficiency, whereas finer resolutions improved detection at the expense of processing time. Coarser resolutions (≥1 m) reduced accuracy due to spatial averaging and plot-mixing effects. These findings highlight the need to optimize UAS flight altitude and sensor configuration to achieve efficient, scalable, and precise cotton water-stress assessment and yield prediction. Full article

(This article belongs to the Special Issue Applications of Unmanned Aerial Remote Sensing in Precision Agriculture)

► Show Figures

Figure 1

23 pages, 6822 KB

Open AccessArticle

From Retrieval to Fate: UAV-Based Hyperspectral Remote Sensing of Soil Nitrogen and Its Leaching Risks in a Wheat-Maize Rotation System

by Zilong Zhang, Shiqin Wang, Jingjin Ma, Chunying Wang, Zhixiong Zhang, Xiaoxin Li, Wenbo Zheng and Chunsheng Hu

Remote Sens. 2025, 17(24), 3956; https://doi.org/10.3390/rs17243956 - 7 Dec 2025

Viewed by 1022

Abstract

Spatiotemporally continuous monitoring of soil nitrogen is essential for rational farmland nitrogen management and non-point source pollution control. This study focused on a typical wheat-maize rotation system in the North China Plain under four nitrogen fertilizer application levels (N0: 0 kg/ha; N200: 200 kg/ha; N400: 400 kg/ha; N600: 600 kg/ha). By integrating soil profile sampling with UAV-based hyperspectral remote sensing, we identified soil nitrogen distribution characteristics and established a retrieval relationship between hyperspectral data and seasonal soil nitrogen dynamics. Results showed that higher nitrogen fertilizer levels significantly increased soil nitrogen content, with N400 and N600 causing nitrate nitrogen (NO₃⁻-N) peaks in both surface and deep layers indicating leaching risk. Hyperspectral imagery at the jointing stage, combined with PLSR and XGBoost-SHAP models, effectively retrieved NO₃⁻-N at 0–50 cm depths. Canopy spectral traits correlated with nitrogen leaching and deep accumulation, suggesting they can serve as early indicators of leaching risk. The “sky-ground” collaborative approach provides conceptual and technical support for precise nitrogen management and pollution control. Full article

(This article belongs to the Special Issue Applications of Unmanned Aerial Remote Sensing in Precision Agriculture)

► Show Figures

Figure 1

21 pages, 23619 KB

Open AccessArticle

Optimizing Data Consistency in UAV Multispectral Imaging for Radiometric Correction and Sensor Conversion Models

by Weiguang Yang, Huaiyuan Fu, Weicheng Xu, Jinhao Wu, Shiyuan Liu, Xi Li, Jiangtao Tan, Yubin Lan and Lei Zhang

Remote Sens. 2025, 17(12), 2001; https://doi.org/10.3390/rs17122001 - 10 Jun 2025

Cited by 1 | Viewed by 1745

Abstract

Recent advancements in precision agriculture have been significantly bolstered by the Uncrewed Aerial Vehicles (UAVs) equipped with multispectral sensors. These systems are pivotal in transforming sensor-recorded Digital Number (DN) values into universal reflectance, crucial for ensuring data consistency irrespective of collection time, region, and illumination. This study, conducted across three regions in China using Sequoia and Phantom 4 Multispectral cameras, focused on examining the effects of radiometric correction on data consistency and accuracy, and developing a conversion model for data from these two sensors. Our findings revealed that radiometric correction substantially enhances data consistency in vegetated areas for both sensors, though its impact on non-vegetated areas is limited. Recalibrating reflectance for calibration plates significantly improved the consistency of band values and the accuracy of vegetation index calculations for both cameras. Decision tree and random forest models emerged as more effective for data conversion between the sensors, achieving R² values up to 0.91. Additionally, the P4M generally outperformed the Sequoia in accuracy, particularly with standard reflectance calibration. These insights emphasize the critical role of radiometric correction in UAV remote sensing for precision agriculture, underscoring the complexities of sensor data consistency and the potential for generalization of models across multi-sensor platforms. Full article

(This article belongs to the Special Issue Applications of Unmanned Aerial Remote Sensing in Precision Agriculture)

► Show Figures

Figure 1

28 pages, 12669 KB

Open AccessArticle

Paddy Field Scale Evapotranspiration Estimation Based on Two-Source Energy Balance Model with Energy Flux Constraints and UAV Multimodal Data

by Tian’ao Wu, Kaihua Liu, Minghan Cheng, Zhe Gu, Weihua Guo and Xiyun Jiao

Remote Sens. 2025, 17(10), 1662; https://doi.org/10.3390/rs17101662 - 8 May 2025

Cited by 22 | Viewed by 2209

Abstract

Accurate evapotranspiration (ET) monitoring is important for making scientific irrigation decisions. Unmanned aerial vehicle (UAV) remote sensing platforms allow for the flexible and efficient acquisition of field data, providing a valuable approach for large-scale ET monitoring. This study aims to enhance the accuracy and reliability of ET estimation in rice paddies through two synergistic approaches: (1) integrating the energy flux diurnal variations into the Two-Source Energy Balance (TSEB) model, which considers the canopy and soil temperature components separately, for physical estimation and (2) optimizing the flight altitudes and observation times for thermal infrared (TIR) data acquisition to enhance the data quality. The results indicated that the energy flux in rice paddies followed a single-peak diurnal pattern dominated by net radiation (Rn). The diurnal variation in the ratio of soil heat flux (G) to Rn could be well fitted by the cosine function with a max value and peak time (R² > 0.90). The optimal flight altitude and time (50 m and 11:00 am) for improved identification of temperature differentiation between treatments were further obtained through cross-comparison. These adaptations enabled the TSEB model to achieve a satisfactory accuracy in estimating energy flux compared to the single-source SEBAL model, with R² values of 0.8501 for Rn − G and 0.7503 for latent heat (LE), as well as reduced rRMSE values. In conclusion, this study presents a reliable method for paddy field scale ET estimation based on a calibrated TSEB model. Moreover, the integration of ground and UAV multimodal data highlights its potential for precise irrigation practices and sustainable water resource management. Full article

(This article belongs to the Special Issue Applications of Unmanned Aerial Remote Sensing in Precision Agriculture)

► Show Figures

Figure 1

17 pages, 9448 KB

Open AccessArticle

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

Cited by 1 | Viewed by 2416

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 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

(This article belongs to the Special Issue Applications of Unmanned Aerial Remote Sensing in Precision Agriculture)

► Show Figures

Figure 1

Other

Jump to: Research

32 pages, 3485 KB

Open AccessSystematic Review

A Systematic Review of Available Multispectral UAV Image Datasets for Precision Agriculture Applications

by Andrea Caroppo, Giovanni Diraco and Alessandro Leone

Remote Sens. 2026, 18(4), 659; https://doi.org/10.3390/rs18040659 - 21 Feb 2026

Cited by 3 | Viewed by 1922

Abstract

The proliferation of Unmanned Aerial Vehicles (UAVs) equipped with multispectral imaging sensors has revolutionized data collection in precision agriculture. These platforms provide high-resolution, temporally dense data crucial for monitoring crop health, optimizing resource management, and predicting yield. However, the development and validation of robust data-driven algorithms, from vegetation index analysis to complex deep learning models, are contingent upon the availability of high-quality, standardized, and publicly accessible datasets. This review systematically surveys and characterizes the current landscape of available datasets containing multispectral imagery acquired by UAVs in agricultural contexts. Following guidelines for reporting systematic reviews and meta-analyses (PRISMA methodology), 39 studies were selected and analyzed, categorizing them based on key attributes including spectral bands (e.g., RGB, Red Edge, Near-Infrared), spatial and temporal resolution, types of crops studied, presence of complementary ground-truth data (e.g., biomass, nitrogen content, yield maps), and the specific agricultural tasks they support (e.g., disease detection, weed mapping, water stress assessment). However, the review underscores a critical gap in standardization, with significant variability in data formats, annotation quality, and metadata completeness, which hampers reproducibility and comparative analysis. Furthermore, we identify a need for more datasets targeting specific challenges like early-stage disease identification and anomaly detection in complex crop canopies. Finally, we discuss future directions for the creation of more comprehensive, benchmark-ready open datasets that will be instrumental in accelerating research, fostering collaboration, and bridging the gap between algorithmic innovation and practical agricultural deployment. This work serves as a foundational guide for researchers and practitioners seeking suitable data for their work and contributes to the ongoing effort of standardizing open data practices in agricultural remote sensing. Full article

(This article belongs to the Special Issue Applications of Unmanned Aerial Remote Sensing in Precision Agriculture)

► Show Figures

Journal Menu

Journal Browser

Applications of Unmanned Aerial Remote Sensing in Precision Agriculture

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (10 papers)

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI