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Remote Sensing
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Aerial Remote Sensing System for Agriculture
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Aerial Remote Sensing System for 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: 20 August 2024 | Viewed by 3580

Special Issue Editors

Dr. Giuseppe Rossi

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Guest Editor
Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Via San Bonaventura 13, 50145 Florence, Italy
Interests: topography; remote sensing; environmental monitoring and analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gabriel Araújo e Silva Ferraz

E-Mail Website
Guest Editor
Department of Agricultural Engineering, School of Engineering, Federal University of Lavras—UFLA, P.O. Box 3037, Lavras 37200-900, Brazil
Interests: remote sensing; UAV in agriculture and livestock; digital and precision farming and livestock
Special Issues, Collections and Topics in MDPI journals
Dr. Leonardo Conti

E-Mail Website
Guest Editor
Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Via San Bonaventura 13, 50145 Florence, Italy
Interests: geographic Information systems; remote sensing; GNSS; environmental monitoring; rural landscape analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Diego Bedin Marin

E-Mail Website
Guest Editor
Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Via San Bonaventura 13, 50145 Florence, Italy
Interests: UAV; remote sensing; digital and precision agriculture; spectral analysis and machine learning

Special Issue Information

Dear Colleagues,

Remote Sensing technologies in precision agriculture have increased exponentially in recent decades. The unprecedented availability of spectral sensors has promoted the use of aerial remote sensing systems in many applications in agriculture, including pest and disease management, irrigation management, nutrient application, and yield prediction. The great advantage of this technology over traditional crop monitoring methods is the rapid and continuous data collection, allowing farmers, researchers and rural professionals to better understand the field studied, having this information in real-time or in a relatively short time data processing.

However, new challenges have also emerged, as the aerial remote sensing systems generate a large volume of spectral data due to the high spatial, spectral and temporal resolutions indispensable for the application in precision agriculture. In view of this, it has been necessary to use data processing techniques, such as big data analysis, artificial intelligence, deep learning, machine learning and other intelligent technologies, to extract useful information from the large volume of data and assist in the decision process for field crops.

This Special Edition is intended for a global research community involved in data analysis and processing and developing innovative aerial remote sensing system solutions for agriculture. Contributions may include, but are not limited to: UAS applied in the detection of pests and diseases, weeds, estimation of planted area and productivity, monitoring of plant health, detection of failures in irrigation, fertilization or soil preparation, analysis of coverage vegetation, topography, drainage and soil type, spectral data analysis, hyperspectral, multispectral and thermal image processing and UAV design for agricultural uses.

Dr. Giuseppe Rossi
Prof. Dr. Gabriel Araújo e Silva Ferraz
Dr. Leonardo Conti
Dr. Diego Bedin Marin
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 100 words) can be sent to the Editorial Office for announcement on this website.

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

  • unmanned aerial systems (UAS)
  • remote sensing
  • precision agriculture
  • digital agriculture
  • ultra-high spatial resolution
  • hyperspectral images
  • multispectral images
  • RGB images
  • vegetation indices
  • machine and deep learning

Published Papers (3 papers)

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Research

22 pages, 10945 KiB  
Article
Analysis of the Influence of Polarization Measurement Errors on the Parameter and Characteristics Measurement of the Fully Polarized Entomological Radar
by Muyang Li, Teng Yu, Rui Wang, Weidong Li, Fan Zhang and Chunfeng Wu
Remote Sens. 2024, 16(7), 1220; https://doi.org/10.3390/rs16071220 - 30 Mar 2024
Viewed by 398
Abstract
Measuring the orientation, mass and body length of migratory insects through entomological radar is crucial for early warnings of migratory pests. The fully polarized entomological radar is an efficient device for observing migratory insects by calculating insect parameters through the scattering matrix obtained [...] Read more.
Measuring the orientation, mass and body length of migratory insects through entomological radar is crucial for early warnings of migratory pests. The fully polarized entomological radar is an efficient device for observing migratory insects by calculating insect parameters through the scattering matrix obtained from the target. However, the measured target scattering matrix will be affected by system polarization measurement errors, leading to errors in insect parameter calculation, while the related analysis is currently relatively limited. Therefore, the scattering matrix measurement process is modeled, followed by an analysis of the effects of different errors on orientation, mass and body length estimation. The influence of polarization measurement errors on insect scattering characteristics is also analyzed. The results present that for fixed polarization measurement errors, the measurement errors of insect orientation, mass and body length will vary with insect orientation in specific patterns, and the distribution of measured insect parameters will be drastically distorted compared to the true parameter distribution. In addition, polarization measurement errors could seriously disrupt the reciprocity and bilateral symmetry of the measured insect scattering matrix. These analyses and conclusions provide a good basis for eliminating the effects of polarization measurement errors and improving the accuracy of insect parameter measurement. Full article
(This article belongs to the Special Issue Aerial Remote Sensing System for Agriculture)
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32 pages, 15910 KiB  
Article
Evaluating the Efficacy of Segment Anything Model for Delineating Agriculture and Urban Green Spaces in Multiresolution Aerial and Spaceborne Remote Sensing Images
by Baoling Gui, Anshuman Bhardwaj and Lydia Sam
Remote Sens. 2024, 16(2), 414; https://doi.org/10.3390/rs16020414 - 20 Jan 2024
Viewed by 1506
Abstract
Segmentation of Agricultural Remote Sensing Images (ARSIs) stands as a pivotal component within the intelligent development path of agricultural information technology. Similarly, quick and effective delineation of urban green spaces (UGSs) in high-resolution images is also increasingly needed as input in various urban [...] Read more.
Segmentation of Agricultural Remote Sensing Images (ARSIs) stands as a pivotal component within the intelligent development path of agricultural information technology. Similarly, quick and effective delineation of urban green spaces (UGSs) in high-resolution images is also increasingly needed as input in various urban simulation models. Numerous segmentation algorithms exist for ARSIs and UGSs; however, a model with exceptional generalization capabilities and accuracy remains elusive. Notably, the newly released Segment Anything Model (SAM) by META AI is gaining significant recognition in various domains for segmenting conventional images, yielding commendable results. Nevertheless, SAM’s application in ARSI and UGS segmentation has been relatively limited. ARSIs and UGSs exhibit distinct image characteristics, such as prominent boundaries, larger frame sizes, and extensive data types and volumes. Presently, there is a dearth of research on how SAM can effectively handle various ARSI and UGS image types and deliver superior segmentation outcomes. Thus, as a novel attempt in this paper, we aim to evaluate SAM’s compatibility with a wide array of ARSI and UGS image types. The data acquisition platform comprises both aerial and spaceborne sensors, and the study sites encompass most regions of the United States, with images of varying resolutions and frame sizes. It is noteworthy that the segmentation effect of SAM is significantly influenced by the content of the image, as well as the stability and accuracy across images of different resolutions and sizes. However, in general, our findings indicate that resolution has a minimal impact on the effectiveness of conditional SAM-based segmentation, maintaining an overall segmentation accuracy above 90%. In contrast, the unsupervised segmentation approach, SAM, exhibits performance issues, with around 55% of images (3 m and coarser resolutions) experiencing lower accuracy on low-resolution images. Whereas frame size exerts a more substantial influence, as the image size increases, the accuracy of unsupervised segmentation methods decreases extremely fast, and conditional segmentation methods also show some degree of degradation. Additionally, SAM’s segmentation efficacy diminishes considerably in the case of images featuring unclear edges and minimal color distinctions. Consequently, we propose enhancing SAM’s capabilities by augmenting the training dataset and fine-tuning hyperparameters to align with the demands of ARSI and UGS image segmentation. Leveraging the multispectral nature and extensive data volumes of remote sensing images, the secondary development of SAM can harness its formidable segmentation potential to elevate the overall standard of ARSI and UGS image segmentation. Full article
(This article belongs to the Special Issue Aerial Remote Sensing System for Agriculture)
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29 pages, 15594 KiB  
Article
A Study on Leveraging Unmanned Aerial Vehicle Collaborative Driving and Aerial Photography Systems to Improve the Accuracy of Crop Phenotyping
by Kangbeen Lee and Xiongzhe Han
Remote Sens. 2023, 15(15), 3903; https://doi.org/10.3390/rs15153903 - 07 Aug 2023
Viewed by 888
Abstract
Unmanned aerial vehicle (UAV)-based aerial images have enabled a prediction of various factors that affect crop growth. However, the single UAV system leaves much to be desired; the time lag between images affects the accuracy of crop information, lowers the image registration quality [...] Read more.
Unmanned aerial vehicle (UAV)-based aerial images have enabled a prediction of various factors that affect crop growth. However, the single UAV system leaves much to be desired; the time lag between images affects the accuracy of crop information, lowers the image registration quality and a maximum flight time of 20–25 min, and limits the mission coverage. A multiple UAV system developed from our previous study was used to resolve the problems centered on image registration, battery duration and to improve the accuracy of crop phenotyping. The system can generate flight routes, perform synchronous flying, and ensure capturing and safety protocol. Artificial paddy plants were used to evaluate the multiple UAV system based on leaf area index (LAI) and crop height measurements. The multiple UAV system exhibited lower error rates on average than the single UAV system, with 13.535% (without wind effects) and 17.729–19.693% (with wind effects) for LAI measurements and 5.714% (without wind effect) and 4.418% (with wind effects) for crop’s height measurements. Moreover, the multiple UAV system reduced the flight time by 66%, demonstrating its ability to overcome battery-related barriers. The developed multiple UAV collaborative system has enormous potential to improve crop growth monitoring by addressing long flight time and low-quality phenotyping issues. Full article
(This article belongs to the Special Issue Aerial Remote Sensing System for Agriculture)
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