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Agronomy
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Sky Guardians of Agriculture: Drones for Efficient Plant Health Surveillance
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Sky Guardians of Agriculture: Drones for Efficient Plant Health Surveillance

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

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1727

Special Issue Editors

Prof. Dr. Junying Chen

E-Mail Website
Guest Editor
1. Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of the Ministry of Education, Northwest A&F University, Yangling 712100, China
2. College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
Interests: UAV; remote sensing; crop monitoring
Dr. Jiang Bian

E-Mail Website
Guest Editor
1. Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of the Ministry of Education, Northwest A&F University, Yangling 712100, China
2. College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
Interests: UAV; remote sensing; crop monitoring
Dr. Xin Hui

E-Mail Website
Guest Editor
1. Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of the Ministry of Education, Northwest A&F University, Yangling 712100, China
2. College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100, China
Interests: smart water conservancy; green and intelligent irrigation technologies and equipment; agricultural remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern agriculture faces mounting pressures from climate change and food security demands, necessitating transformative approaches to crop management. Drones, having evolved from basic imaging tools to sophisticated agricultural assets through advances in sensors, artificial intelligence, and automation, now offer unprecedented capabilities for plant health surveillance. This Special Issue compiles cutting-edge research on drone systems designed for efficient, scalable monitoring; diagnosis; and management of plant health across diverse agroecosystems. We seek original research and reviews demonstrating innovations in hyperspectral, multispectral, thermal infrared phenotyping, AI-driven biotic/abiotic stress detection, real-time data processing, autonomous scouting, sensor fusion, and farm management integration. Contributions must exhibit scientific rigor and practical relevance, focusing on early stress detection, precision disease management, nutrient monitoring, health-linked yield prediction, and surveillance efficiency optimization. Submissions covering novel methodologies, validation frameworks, and field-to-algorithm translation are particularly encouraged.

Prof. Dr. Junying Chen
Dr. Jiang Bian
Dr. Xin Hui
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. Agronomy 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

  • drones
  • hyperspectral
  • multispectral
  • thermal infrared
  • early stress detection
  • precision disease management
  • nutrient monitoring
  • yield prediction

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Published Papers (2 papers)

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Research

19 pages, 3682 KB  
Article
Estimation of Cotton Above-Ground Biomass Based on Fusion of UAV Spectral and Texture Features
by Guldana Sarsen, Qiuxiang Tang, Yabin Li, Longlong Bao, Yuhang Xu, Guangyun Sun, Jianwen Wu, Yierxiati Abulaiti, Qingqing Lv, Fubin Liang, Na Zhang, Rensong Guo, Liang Wang, Jianping Cui and Tao Lin
Agronomy 2026, 16(6), 668; https://doi.org/10.3390/agronomy16060668 - 22 Mar 2026
Viewed by 586
Abstract
Cotton above-ground biomass (AGB) is a key indicator of crop growth and yield potential. Traditional monitoring methods are labor-intensive and destructive, limiting their suitability for precision agriculture. This study developed a high-precision, non-destructive model for estimating cotton AGB by integrating spectral and texture [...] Read more.
Cotton above-ground biomass (AGB) is a key indicator of crop growth and yield potential. Traditional monitoring methods are labor-intensive and destructive, limiting their suitability for precision agriculture. This study developed a high-precision, non-destructive model for estimating cotton AGB by integrating spectral and texture features derived from UAV multispectral and RGB images. UAV data were collected at major growth stages in 2024. Eight vegetation indices (VIs) and eight texture features (TFs) were extracted. Four machine learning algorithms—support vector regression (SVR), random forest regression (RFR), partial least squares regression (PLSR), and extreme gradient boosting (XGB)—were evaluated using independent validation data. Models based on fused spectral and texture features outperformed single-feature models. RFR achieved the best performance (R2 = 0.811; RMSE = 2.931 t ha−1). Texture features alone also showed strong predictive capability (R2 = 0.789), highlighting their value in capturing canopy structural information. These results demonstrate that spectral–texture fusion significantly improves cotton AGB estimation and that RFR provides a robust modeling framework for UAV-based crop monitoring. Full article
(This article belongs to the Special Issue Sky Guardians of Agriculture: Drones for Efficient Plant Health Surveillance)
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24 pages, 43005 KB  
Article
Accurate Estimation of Spring Maize Aboveground Biomass in Arid Regions Based on Integrated UAV Remote Sensing Feature Selection
by Fengxiu Li, Yanzhao Guo, Yingjie Ma, Ning Lv, Zhijian Gao, Guodong Wang, Zhitao Zhang, Lei Shi and Chongqi Zhao
Agronomy 2026, 16(2), 219; https://doi.org/10.3390/agronomy16020219 - 16 Jan 2026
Cited by 1 | Viewed by 727
Abstract
Maize is one of the top three crops globally, ranking only behind rice and wheat, making it an important crop of interest. Aboveground biomass is a key indicator for assessing maize growth and its yield potential. This study developed an efficient and stable [...] Read more.
Maize is one of the top three crops globally, ranking only behind rice and wheat, making it an important crop of interest. Aboveground biomass is a key indicator for assessing maize growth and its yield potential. This study developed an efficient and stable biomass prediction model to estimate the aboveground biomass (AGB) of spring maize (Zea mays L.) under subsurface drip irrigation in arid regions, based on UAV multispectral remote sensing and machine learning techniques. Focusing on typical subsurface drip-irrigated spring maize in arid Xinjiang, multispectral images and field-measured AGB data were collected from 96 sample points (selected via stratified random sampling across 24 plots) over four key phenological stages in 2024 and 2025. Sixteen vegetation indices were calculated and 40 texture features were extracted using the gray-level co-occurrence matrix method, while an integrated feature-selection strategy combining Elastic Net and Random Forest was employed to effectively screen key predictor variables. Based on the selected features, six machine learning models were constructed, including Elastic Net Regression (ENR), Gradient Boosting Decision Trees (GBDT), Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGB). Results showed that the fused feature set comprised four vegetation indices (GRDVI, RERVI, GRVI, NDVI) and five texture features (R_Corr, NIR_Mean, NIR_Vari, B_Mean, B_Corr), thereby retaining red-edge and visible-light texture information highly sensitive to AGB. The GPR model based on the fused features exhibited the best performance (test set R2 = 0.852, RMSE = 2890.74 kg ha−1, MAE = 1676.70 kg ha−1), demonstrating high fitting accuracy and stable predictive ability across both the training and test sets. Spatial inversions over the two growing seasons of 2024 and 2025, derived from the fused-feature GPR optimal model at four key phenological stages, revealed pronounced spatiotemporal heterogeneity and stage-dependent dynamics of spring maize AGB: the biomass accumulates rapidly from jointing to grain filling, slows thereafter, and peaks at maturity. At a constant planting density, AGB increased markedly with nitrogen inputs from N0 to N3 (420 kg N ha−1), with the high-nitrogen N3 treatment producing the greatest biomass; this successfully captured the regulatory effect of the nitrogen gradient on maize growth, provided reliable data for variable-rate fertilization, and is highly relevant for optimizing water–fertilizer coordination in subsurface drip irrigation systems. Future research may extend this integrated feature selection and modeling framework to monitor the growth and estimate the yield of other crops, such as rice and cotton, thereby validating its generalizability and robustness in diverse agricultural scenarios. Full article
(This article belongs to the Special Issue Sky Guardians of Agriculture: Drones for Efficient Plant Health Surveillance)
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