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Agriculture
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Unmanned Aerial System for Crop Monitoring in Precision Agriculture
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Unmanned Aerial System for Crop Monitoring in Precision Agriculture

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Artificial Intelligence and Digital Agriculture".

Deadline for manuscript submissions: 25 September 2026 | Viewed by 1641

Special Issue Editors

Dr. Hengbiao Zheng

E-Mail Website
Guest Editor
College of Smart Agriculture, National Engineering and Technology Center for Information Agriculture (NETCIA), Nanjing Agricultural University, Nanjing 211800, China
Interests: UAV; precision agriculture; agricultural remote sensing; image analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xia Yao

E-Mail Website
Guest Editor
College of Agriculture, National Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
Interests: crop SIF; phenotyping on LiDAR and UAV platforms; quantification of crop properties; disease surveillance; hyperspectral remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unmanned Aerial Systems (UASs) have become a cornerstone of modern precision agriculture, fundamentally transforming the methods of crop monitoring and management. They provide an unparalleled platform for capturing high-resolution, real-time data on crop health, growth status, and field conditions. Therefore, a deeper understanding of how UAS can enhance the assessment of crop growth, nutrient status, stress conditions, yield, and quality is of crucial importance.

This Special Issue focuses on the application of UAS in crop phenotyping, growth monitoring, disease detection, and yield and quality estimation. Significant emphasis is placed on advanced data analytics, including machine learning and deep learning algorithms, for the automated interpretation of complex UAS-derived datasets to develop predictive models, yield maps, and precise application prescriptions. We welcome all types of contributions, such as original research articles, opinions, and reviews.

Dr. Hengbiao Zheng
Prof. Dr. Xia Yao
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. Agriculture 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 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 systems (UASs)
  • precision agriculture
  • crop monitoring
  • stress detection
  • yield prediction
  • machine learning
  • deep learning

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Published Papers (2 papers)

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Research

19 pages, 2510 KB  
Article
Grain Yield Estimation of Rice Germplasm Resources Using Time-Series UAV Imagery and Dynamic Clustering Process
by Qi Ke, Di Wang, Yan Zhao, Caili Guo, Xiaoxu Han, Ankang Zhang, Chongya Jiang, Xia Yao, Tao Cheng, Weixing Cao, Yan Zhu and Hengbiao Zheng
Agriculture 2026, 16(10), 1056; https://doi.org/10.3390/agriculture16101056 - 12 May 2026
Viewed by 419
Abstract
Traditional methods for measuring rice yield are often labor-intensive, time-consuming, and difficult to implement at scale. Conversely, remote sensing-based yield prediction models typically exhibit limited applicability across diverse genetic materials. In this study, we propose a high-precision yield prediction approach that integrates UAV-based [...] Read more.
Traditional methods for measuring rice yield are often labor-intensive, time-consuming, and difficult to implement at scale. Conversely, remote sensing-based yield prediction models typically exhibit limited applicability across diverse genetic materials. In this study, we propose a high-precision yield prediction approach that integrates UAV-based time-series imagery with dynamic process clustering. Field experiments were conducted over two years involving 630 rice germplasm accessions in Rugao and Huaian, Jiangsu Province. UAV-mounted RGB and multispectral cameras were employed to acquire canopy imagery throughout the rice growth period. A range of features, including spectral reflectance, vegetation indices, canopy height (CH), and canopy volume (CV), were extracted from the UAV data. The K-Shape clustering algorithm was applied to dynamically group the temporal growth curves, enabling the construction of a cluster-based yield prediction model. Among the vegetation indices, the Enhanced Vegetation Index (EVI2) demonstrated the best performance (R2 = 0.73, RMSE = 599.53 kg/hm2). Models based on temporal features of CH and CV showed satisfactory accuracy (R2 = 0.70, RMSE = 640.96 kg/hm2). Notably, a dual-modal model combining vegetation indices with structural parameters significantly improved predictive performance (R2 = 0.80, RMSE = 511.42 kg/hm2). This study demonstrates that multi-feature cluster analysis enhances the accuracy and robustness of yield prediction models across diverse genotypes. The proposed methodology provides valuable technical support for high-yield rice breeding initiatives. Full article
(This article belongs to the Special Issue Unmanned Aerial System for Crop Monitoring in Precision Agriculture)
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18 pages, 3498 KB  
Article
Improved Estimation of Cotton Aboveground Biomass Using a New Developed Multispectral Vegetation Index and Particle Swarm Optimization
by Guanyu Wu, Mingyu Hou, Yuqiao Wang, Hongchun Sun, Liantao Liu, Ke Zhang, Lingxiao Zhu, Xiuliang Jin, Cundong Li and Yongjiang Zhang
Agriculture 2025, 15(24), 2608; https://doi.org/10.3390/agriculture15242608 - 17 Dec 2025
Viewed by 642
Abstract
Accurate and rapid estimation of aboveground biomass (AGB) in cotton is crucial for precise agricultural management. However, current AGB estimation methods are limited by data homogeneity and insufficient model accuracy, which fail to comprehensively reflect the cotton growth status. This study introduces a [...] Read more.
Accurate and rapid estimation of aboveground biomass (AGB) in cotton is crucial for precise agricultural management. However, current AGB estimation methods are limited by data homogeneity and insufficient model accuracy, which fail to comprehensively reflect the cotton growth status. This study introduces a novel approach by coupling cotton canopy Soil and Plant Analyzer Development (SPAD) values with multispectral (MS) data to achieve precise estimation of cotton AGB. Two experimental treatments, involving varied nitrogen fertilizer rates and organic manure applications, were conducted from 2022 to 2023. MS data from UAVs were collected across multiple cotton growth stages, while AGB and canopy SPAD values were synchronously measured. Using the coefficient of variation method, SPAD values were coupled with existing vegetation indices to develop a novel vegetation index termed CGSIVI. Moreover, the applicability of various machine learning algorithms—including Random Forest Regressor (RFR), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Particle Swarm Optimization-XGBoost (PSO-XGBoost), and Particle Swarm Optimization-CatBoost (PSO-CatBoost)—was evaluated for inverting cotton AGB. The results indicated that, compared to the original vegetation indices, the correlation between the improved vegetation index (CGSIVI) and AGB was enhanced by 13.60% overall, with the CGSICIre exhibiting the highest correlation with cotton AGB (R2 = 0.87). The overall AGB estimation accuracy across different growth stages, spanning the entire growth period, ranged from 0.768 to 0.949, peaking during the flowering stage. Furthermore, when the CGSIVI was used as an input parameter in comparisons of different machine learning algorithms, the PSO-XGBoost algorithm demonstrated superior estimation accuracy across the entire growth stage and within individual growth stages. This high-throughput crop phenotyping analysis method enables rapid and accurate estimation. It reveals the spatial heterogeneity of cotton growth status, thereby providing a powerful tool for accurately identifying growth differences in the field. Full article
(This article belongs to the Special Issue Unmanned Aerial System for Crop Monitoring in Precision Agriculture)
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