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Remote Sensing
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Intelligent UAV Remote Sensing for Next-Generation Precision Agriculture
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Intelligent UAV Remote Sensing for Next-Generation Precision Agriculture

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: closed (30 April 2026) | Viewed by 825

Special Issue Editors

Prof. Dr. Francisco Javier Mesas Carrascosa

E-Mail Website
Guest Editor
Department of Graphic Engineering and Geomatics, Campus de Rabanales, University of Cordoba, 14071 Cordoba, Spain
Interests: remote sensing; geomatic; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Fernando Pérez Porras

E-Mail Website
Guest Editor
Department of Graphic Engineering and Geomatics, Campus de Rabanales, University of Cordoba, 14071 Cordoba, Spain
Interests: remote sensing; geomatic; precision agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The necessity for sustainable agricultural practices and the global challenge of food security require management and monitoring solutions with very high spatiotemporal resolution. These solutions must strengthen and support the differential management of crops, consolidating Agriculture 4.0 and advancing towards 5.0. This Special Issue aims to gather cutting-edge research by combining Artificial Intelligence and remote sensing carried out by Unmanned Aerial Vehicles (UAVs).

UAV systems provide an unmatched platform for recording LiDAR, thermal, hyperspectral, and multispectral data, with unparalleled flexibility at both the spatial and temporal levels. However, to transform these datasets into valuable information that assists decision-making for precise and automated agronomic actions, the development of effective and intelligent algorithms is essential.

The advances in methodologies and practical applications of the intelligent use of UAVs in agriculture are the proposed topics for the submission of original manuscripts. Areas of interest include:

  • Artificial intelligence and deep learning algorithms for the identification, prediction, or early detection of water stress, pests, and/or diseases in crops.
  • Integration and fusion of multi-sensor data (e.g., UAV LiDAR, thermal, multispectral, and RGB) for the accurate description of vegetation parameters related to structure, vigor, nutrition, among others.
  • Creation and development of autonomous and real-time systems for UAV route planning, variable rate application of inputs, and connection/interaction with other systems.
  • Application of advanced processing techniques to model and map weeds and distinguish them from the main crop.
  • Use of UAV-borne sensors for yield assessment.

Prof. Dr. Francisco Javier Mesas Carrascosa
Dr. Fernando Pérez Porras
Guest Editor

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
  • 3D point cloud analysis
  • spectral and structural data fusion
  • artificial intelligence
  • UAV
  • geospatial data

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Published Papers (1 paper)

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Research

23 pages, 13051 KB  
Article
BAWSeg: A UAV Multispectral Benchmark for Barley Weed Segmentation
by Haitian Wang, Xinyu Wang, Muhammad Ibrahim, Dustin Severtson and Ajmal Mian
Remote Sens. 2026, 18(6), 915; https://doi.org/10.3390/rs18060915 - 17 Mar 2026
Viewed by 522
Abstract
Accurate weed mapping in cereal fields requires pixel-level segmentation from unmanned aerial vehicle (UAV) imagery that remains reliable across fields, seasons, and illumination. Existing multispectral pipelines often depend on thresholded vegetation indices, which are brittle under radiometric drift and mixed crop–weed pixels, or [...] Read more.
Accurate weed mapping in cereal fields requires pixel-level segmentation from unmanned aerial vehicle (UAV) imagery that remains reliable across fields, seasons, and illumination. Existing multispectral pipelines often depend on thresholded vegetation indices, which are brittle under radiometric drift and mixed crop–weed pixels, or on single-stream convolutional neural network (CNN) and Transformer backbones that ingest stacked bands and indices, where radiance cues and normalized index cues interfere and reduce sensitivity to small weed clusters embedded in crop canopy. We propose VISA (Vegetation Index and Spectral Attention), a two-stream segmentation network that decouples these cues and fuses them at native resolution. The radiance stream learns from calibrated five-band reflectance using local residual convolutions, channel recalibration, spatial gating, and skip-connected decoding, which preserve fine textures, row boundaries, and small weed structures that are often weakened after ratio-based index compression. The index stream operates on vegetation-index maps with windowed self-attention to model local structure efficiently, state-space layers to propagate field-scale context without quadratic attention cost, and Slot Attention to form stable region descriptors that improve discrimination of sparse weeds under canopy mixing. To support supervised training and deployment-oriented evaluation, we introduce BAWSeg, a four-year UAV multispectral dataset collected over commercial barley paddocks in Western Australia, providing radiometrically calibrated blue, green, red, red edge, and near-infrared orthomosaics, derived vegetation indices, and dense crop, weed, and other labels with leakage-free block splits. On BAWSeg, VISA achieves 75.6% mean Intersection over Union (mIoU) and 63.5% weed Intersection over Union (IoU) with 22.8 M parameters, outperforming a multispectral SegFormer-B1 baseline by 1.2 mIoU and 1.9 weed IoU. Under cross-plot and cross-year protocols, VISA maintains 71.2% and 69.2% mIoU, respectively. The full BAWSeg benchmark dataset, VISA code, trained model weights, and protocol files will be released upon publication. Full article
(This article belongs to the Special Issue Intelligent UAV Remote Sensing for Next-Generation Precision Agriculture)
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