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Remote Sensing
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Application of UAV Images in Precision Agriculture
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Application of UAV Images in Precision Agriculture

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 3539

Special Issue Editors

Dr. Ganesh Bora

E-Mail Website
Guest Editor
Associate Vice Chancellor for Research and Innovation, Fayetteville State University, Fayetteville, NC, USA
Interests: precision agriculture; remote sensing and UAS; data science; AI and machine learning; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Yali Zhang

E-Mail Website
Guest Editor
1. College of Engineering, South China Agricultural University, Wushan Road, Guangzhou 510642, China
2. National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangzhou 510642, China
Interests: UAV; precision spraying; sensors and controls
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart technologies, such as unmanned aerial vehicles (UAVs), remote sensing, artificial intelligence, machine learning, autonomous robotics, and Internet of Things, have become an integral part of precision agriculture and provide accessible, data-driven solutions for climate-resilient agricultural productivity. Quality data transformed to a useful format are significant to sustainable crop and animal management, used to increase productivity, environmental health, and the development of natural resources.

This Special Issue of Remote Sensing focuses on the “Application of UAV Images in Precision Agriculture” to deal with broad aspects of timely data collection and utilization for resilient precision farming.

Dr. Ganesh Bora
Dr. Yali Zhang
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

  • UAV
  • precision agriculture
  • remote sensing
  • AI
  • image processing
  • geospatial data

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Published Papers (3 papers)

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Research

26 pages, 8605 KB  
Article
The Application of Amino Acids as a Sustainable Strategy for Managing Water Stress in Vineyards
by Fabrício Lopes Macedo, Carla Ragonezi, José Filipe Teixeira Ganança, Humberto Nóbrega, José G. R. de Freitas, Andrés A. Borges, David Jiménez-Arias and Miguel A. A. Pinheiro de Carvalho
Remote Sens. 2026, 18(4), 641; https://doi.org/10.3390/rs18040641 - 19 Feb 2026
Viewed by 415
Abstract
Water scarcity increasingly threatens viticulture in the Macaronesian region due to climatic variability and recurrent droughts. This study evaluated the physiological and productive responses of grapevines (Vitis vinifera L.) to foliar applications of two amino acid-based biostimulants, pyroglutamic acid and pipecolic acid, [...] Read more.
Water scarcity increasingly threatens viticulture in the Macaronesian region due to climatic variability and recurrent droughts. This study evaluated the physiological and productive responses of grapevines (Vitis vinifera L.) to foliar applications of two amino acid-based biostimulants, pyroglutamic acid and pipecolic acid, under contrasting water availability conditions on Madeira Island, Portugal. Three non-irrigated treatments were arranged in a randomized complete block design: T1 (no irrigation and no amino acids), T2 (pyroglutamic acid, without irrigation), and T3 (pipecolic acid, without irrigation), while conventional irrigation (T4) was included as a non-randomized reference. Agronomic parameters and UAV-derived multispectral and thermal data were analyzed during the 2023 (moderate drought) and 2024 (severe drought) growing seasons. Vegetation indices (NDVI, GNDVI, NDRE, NGRDI, and GLI) and the Simplified Crop Water Stress Index (CWSIsi) were used to assess canopy vigor and plant water status. In 2023, T4 showed significantly higher bunch number and total yield, whereas differences among non-irrigated treatments were not statistically significant. Nevertheless, T2 showed consistent numerical trends toward higher yield components and a comparatively more stable canopy thermal response than the untreated control. In 2024, severe drought reduced productivity across all treatments, with no significant difference detected. Yield components were generally strongly correlated, while CWSIsi was negatively associated with vegetation indices, particularly under moderate drought. The NGRDI demonstrated potential as a low-cost RGB-based indicator but requires cautious interpretation. Overall, pyroglutamic acid may represent a complementary strategy to irrigation and UAV-based precision monitoring in drought-prone viticulture, although confirmation through longer-term and higher-powered field studies is required. Full article
(This article belongs to the Special Issue Application of UAV Images in Precision Agriculture)
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24 pages, 2210 KB  
Article
Deep Transfer Learning for UAV-Based Cross-Crop Yield Prediction in Root Crops
by Suraj A. Yadav, Yanbo Huang, Kenny Q. Zhu, Rayyan Haque, Wyatt Young, Lorin Harvey, Mark Hall, Xin Zhang, Nuwan K. Wijewardane, Ruijun Qin, Max Feldman, Haibo Yao and John P. Brooks
Remote Sens. 2025, 17(24), 4054; https://doi.org/10.3390/rs17244054 - 17 Dec 2025
Cited by 1 | Viewed by 976
Abstract
Limited annotated data often constrain accurate yield prediction in underrepresented crops. To address this challenge, we developed a cross-crop deep transfer learning (TL) framework that leverages potato (Solanum tuberosum L.) as the source domain to predict sweet potato (Ipomoea batatas L.) [...] Read more.
Limited annotated data often constrain accurate yield prediction in underrepresented crops. To address this challenge, we developed a cross-crop deep transfer learning (TL) framework that leverages potato (Solanum tuberosum L.) as the source domain to predict sweet potato (Ipomoea batatas L.) yield using multi-temporal uncrewed aerial vehicle (UAV)-based multispectral imagery. A hybrid convolutional–recurrent neural network (CNN–RNN–Attention) architecture was implemented with a robust parameter-based transfer strategy to ensure temporal alignment and feature-space consistency across crops. Cross-crop feature migration analysis showed that predictors capturing canopy vigor, structure, and soil–vegetation contrast exhibited the highest distributional similarity between potato and sweet potato. In comparison, pigment-sensitive and agronomic predictors were less transferable. These robustness patterns were reflected in model performance, as all architectures showed substantial improvement when moving from the minimal 3 predictor subset to the 5–7 predictor subsets, where the most transferable indices were introduced. The hybrid CNN–RNN–Attention model achieved peak accuracy (R2≈0.64 and RMSE ≈ 18%) using time-series data up to the tuberization stage with only 7 predictors. In contrast, convolutional neural network (CNN), bidirectional gated recurrent unit (BiGRU), and bidirectional long short-term memory (BiLSTM) baseline models required 11–13 predictors to achieve comparable performance and often showed reduced or unstable accuracy at higher dimensionality due to redundancy and domain-shift amplification. Two-way ANOVA further revealed that cover crop type significantly influenced yield, whereas nitrogen rate and the interaction term were not significant. Overall, this study demonstrates that combining robustness-aware feature design with hybrid deep TL model enables accurate, data-efficient, and physiologically interpretable yield prediction in sweet potato, offering a scalable pathway for applying TL in other underrepresented root and tuber crops. Full article
(This article belongs to the Special Issue Application of UAV Images in Precision Agriculture)
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Graphical abstract

20 pages, 3631 KB  
Article
From Experimental Field to Real Field: Monitoring Wheat Stripe Rust Based on Optimized Hyperspectral Vegetation Index
by Meng Wang, Dongrui Han, Rui Gao, Tao Liu, Wenjie Feng, Fei Wang, Zhuoran Zhang and Junyong Zhang
Remote Sens. 2025, 17(23), 3798; https://doi.org/10.3390/rs17233798 - 23 Nov 2025
Cited by 1 | Viewed by 910
Abstract
Wheat stripe rust is an important fungal disease that threatens global wheat production, and precise monitoring in field environments is crucial for disease prevention and control. This study proposes a cross-scale monitoring method based on optimized hyperspectral vegetation index to address the issues [...] Read more.
Wheat stripe rust is an important fungal disease that threatens global wheat production, and precise monitoring in field environments is crucial for disease prevention and control. This study proposes a cross-scale monitoring method based on optimized hyperspectral vegetation index to address the issues of low efficiency of traditional monitoring methods and susceptibility of spectral signals to interference in field environments. Through comparative studies between experimental fields (n = 68) and large fields (n = 155), the performance of six vegetation indices was systematically evaluated, and optimized versions were designed. The study mainly found that the Yellow Rust Severity Index optimized (YRSIO) index exhibited the best monitoring performance, with a field determination coefficient R2 of 0.5713 (experimental field R2 = 0.6118). The unmanned aerial vehicle (UAV) hyperspectral system combined with optimized vegetation index can effectively control spectral reflectance fluctuations, with a recognition accuracy of up to 85.2% in severely infected areas. This study also elucidated the three-stage physiological response mechanism of optimizing indicators on disease progression. This study provides key technical support for the practical application of hyperspectral technology in field monitoring of wheat stripe rust, and the proposed research method can be extended to other fields of crop disease monitoring. Full article
(This article belongs to the Special Issue Application of UAV Images in Precision Agriculture)
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