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Integrating UAV-Based and Multi-Source Remote Sensing with Artificial Intelligence for Better Crop Management

A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 15 February 2027 | Viewed by 1697

Editors

Dr. Fan Zhao

E-Mail Website
Guest Editor
Department of Urban Informatics, Shenzhen University, Shenzhen, China
Interests: marine remote sensing; photogrammetry; aquatic ecological monitoring; benthic mapping; coral reef monitoring; UAV applications; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Kangning Cui

E-Mail Website
Guest Editor
Department of Computer Science, Wake Forest University, 1834 Wake Forest Road, Winston-Salem, NC 27109, USA
Interests: remote sensing; ecological monitoring; biodiversity and species distribution; object detection; land cover classification; statistical modeling and simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Jiaqi Wang

E-Mail Website
Guest Editor
Department of Environment Systems, The University of Tokyo, Kashiwa 288-8561, Japan
Interests: remote sensing; coral reef monitoring; underwater image processing; photogrammetry; aquatic ecological monitoring; IoT for aquatic ecologial monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unmanned Aerial Vehicles (UAVs) have become a transformative platform in agricultural remote sensing, offering high flexibility, ultra-high spatial resolution, and timely field-scale data acquisition. As agriculture faces pressures from climate change and resource constraints, precise crop management is increasingly critical. Traditional methods such as field surveys and satellite observations are often limited in resolution or frequency, especially in heterogeneous landscapes. UAVs bridge this gap by enabling detailed monitoring of crop growth, health, and stress throughout the season. Equipped with multispectral, thermal, and other sensors, they capture crop structure and physiological information essential for precision agriculture applications such as variable-rate fertilization, irrigation optimization, and pest detection. Advances in AI and data processing enhance the extraction of agronomic insights from UAV imagery. Integrated with ground data and models, UAV-derived products support multi-scale crop management and decision-making, positioning UAV technology as a key research frontier in agricultural remote sensing.

This Special Issue provides a comprehensive overview of UAV-based remote sensing in crop management across diverse cropping systems and agro-ecologies. It highlights methodological advances, innovative applications, and case studies demonstrating UAV data’s role in improving productivity, resource efficiency, and sustainability. Its scope aligns with that of Remote Sensing by focusing on remote sensing technologies, data analysis, and integrated frameworks. Contributions should emphasize UAV observations while exploring synergies with satellite data, ground measurements, and decision-support systems. This Special Issue bridges technical innovation with agricultural applications to support farmers, researchers, and policymakers.

This Special Issue welcomes original research articles, review papers, and methodological studies. Suggested themes include, but are not limited to, UAV-based monitoring of crop growth, phenology, and spatial variability;  crop health, stress, disease, and pest detection using multispectral, hyperspectral, or thermal UAV data; the estimation of crop biophysical parameters (e.g., LAI, biomass, canopy height, nitrogen status); UAV applications in irrigation management, water stress assessment, and evapotranspiration estimation; yield estimation and in-season yield forecasting using UAV imagery; machine learning and deep learning methods for crop mapping and trait extraction; multi-sensor and multi-platform data fusion (UAV–satellite–ground integration); and UAV-supported precision agriculture and variable-rate management strategies.

Dr. Fan Zhao
Dr. Kangning Cui
Dr. Jiaqi Wang
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-anonymized 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

  • UAV
  • drone-based remote sensing
  • crop management
  • precision agriculture
  • crop monitoring
  • deep learning and machine learning
  • multispectral and hyperspectral imagery
  • agricultural decision support

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

Published Papers (2 papers)

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Research

38 pages, 23781 KB  
Article
Precision Greenhouse Rose Phenotyping from UAV Imagery Using a Multi-Source Dataset and Lightweight BloomRoseNet
by Yingchao Wang, Jun Hao, Peng Zhou, Wei Chen, Shan Sun, Na Li, Feng Xue, Zixiang Qin, Hao Wu and Fan Zhao
Remote Sens. 2026, 18(16), 2704; https://doi.org/10.3390/rs18162704 - 11 Aug 2026
Viewed by 377
Abstract
Accurate detection of blooming roses and flower buds is essential for greenhouse phenotyping, cultivation scheduling, harvest planning, and yield management. However, UAV-derived greenhouse imagery presents major challenges because rose targets are often small, densely distributed, partially occluded, and visually similar to complex backgrounds. [...] Read more.
Accurate detection of blooming roses and flower buds is essential for greenhouse phenotyping, cultivation scheduling, harvest planning, and yield management. However, UAV-derived greenhouse imagery presents major challenges because rose targets are often small, densely distributed, partially occluded, and visually similar to complex backgrounds. This study proposes a lightweight rose detection framework that combines multi-source dataset construction with an improved YOLOv12n-based detector, termed BloomRoseNet. A GreenHouse Rose dataset was constructed by integrating self-collected UAV overhead images, screened RoseTracker images, and supplementary multi-view rose images to increase diversity in scale, growth stage, viewpoint, and background complexity. BloomRoseNet introduces task-oriented improvements for fine-grained feature extraction, adaptive feature fusion, and attention-enhanced detection. The supplementary multi-view data improved precision, recall, and mAP@50 from 85.2%, 82.8%, and 89.2% to 86.1%, 85.3%, and 90.5%, respectively. Compared with the baseline YOLOv12n, BloomRoseNet increased precision, recall, mAP@50, and mAP@50:95 by 3.2, 3.3, 3.6, and 1.6 percentage points, respectively, while reducing parameters from 2.55 M to 2.08 M and model size from 5.5 MB to 4.5 MB. The model also maintained real-time inference capability and stronger robustness under blur, occlusion, and illumination disturbances. The proposed framework provides an effective and practical solution for UAV-based greenhouse rose monitoring and supports precision cultivation management. Full article
(This article belongs to the Special Issue Integrating UAV-Based and Multi-Source Remote Sensing with Artificial Intelligence for Better Crop Management)
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33 pages, 33283 KB  
Article
Using UAV-Based RGB and Multispectral Imagery to Estimate Cotton Above-Ground Biomass by Integrating Multi-Modal Features and Machine Learning Algorithms
by Madjebi Collela Be, Jie Zhang, Beifang Yang, Shengping Liu, Yingchun Han, Yaping Lei, Xiaoyu Zhi, Shiwu Xiong, Yahui Jiao, Yunzhen Ma, Shilong Shang, Antsa Sarobidy Randrianantenaina, Hamad Khan, Haoshen Zhang, Yaru Wang, Tao Lin and Yabing Li
Remote Sens. 2026, 18(14), 2278; https://doi.org/10.3390/rs18142278 - 8 Jul 2026
Viewed by 732
Abstract
Real-time monitoring of cotton above-ground biomass (AGB) is crucial for monitoring crop growth and optimizing management practices. This study evaluated UAV-based RGB and multispectral (MS) imagery for cotton AGB estimation across multiple growth stages under different planting densities and sowing dates in Anyang, [...] Read more.
Real-time monitoring of cotton above-ground biomass (AGB) is crucial for monitoring crop growth and optimizing management practices. This study evaluated UAV-based RGB and multispectral (MS) imagery for cotton AGB estimation across multiple growth stages under different planting densities and sowing dates in Anyang, China. Spectral features, vegetation indices (VIs), and Gray Level Co-occurrence Matrix (GLCM) texture metrics were extracted and organized into three scenarios: RGB + MS, RGB-only, and MS-only. Recursive feature elimination with cross-validation (RFECV) was applied for feature selection, and six machine learning models were evaluated using both baseline and selected feature sets. Results showed that model performance was strongly influenced by growth stage, sensor configuration, and feature composition. Accuracy was highest at the seedling and squaring stages and decreased at flowering due to canopy complexity and spectral saturation. MS-only and fused features generally performed best at the seedling stage, while RGB-only features were competitive or superior at the squaring stage, highlighting the importance of high-resolution structural information. At flowering, fused RGB–MS features provided the most stable performance, although improvements were limited. RFECV exhibited stage-dependent behavior, improving performance mainly at early growth stages but showing inconsistent benefits later. SHAP analysis revealed a shift from texture-dominated predictors at the seedling stage to balanced feature contributions at squaring and vegetation index (VIs) dominance at flowering. Overall, cotton AGB estimation is a stage-dependent process requiring adaptive sensor and feature selection strategies. Full article
(This article belongs to the Special Issue Integrating UAV-Based and Multi-Source Remote Sensing with Artificial Intelligence for Better Crop Management)
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