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UAV-Based Remote Sensing Applications in Precision Agriculture
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UAV-Based Remote Sensing Applications in Precision Agriculture

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 11940

Special Issue Editor

Dr. Thomas Jarmer

E-Mail Website
Guest Editor
Institute of Computer Science, Remote Sensing and Digital Image Analysis, University of Osnabrück, Wachsbleiche 27, D-49090 Osnabrück, Germany
Interests: remote-sensing-based quantification of soil properties; analysis of hyperspectral data; remote sensing in precision agriculture; integration of remote sensing and GIS; monitoring in remote sensing; degradation and desertification research; laboratory and field spectroscopy

Special Issue Information

Dear Colleagues,

Efficient food production requires a balance between the minimization of negative environmental impact and yield maximization. Ecological conditions and current management techniques have a major influence on the spatial heterogeneity of agricultural fields. Farm management and all inputs should be adapted for this infield variability. Hence, basic information is required to perform site-specific management according to intrafield variability.

Remote sensing data at various scales have already proven to be very useful for agricultural crop monitoring. In particular, UAV remote sensing supports very precise monitoring through lower flight altitudes and high spatial resolution data. Several studies have successfully demonstrated that UAV-based sensors (RGB, multispectral, hyperspectral, thermal LiDAR data) allow crop parameter prediction, yield estimation or biotic stress monitoring. From a methodical perspective, deep learning and artificial intelligence (AI) provide new and very promising methodological approaches for the analysis of UAV remote sensing data. In the context of remote sensing for applications in precision agriculture, more research is required to apply these modern methods to this relevant topic.

Thus, we would like to invite you to share your research and to participate in the submission of articles for this Special Issue with respect (but not limited) to the following topics, related to remote sensing applications in precision agriculture:

  • UAV-based prediction of crop parameters for site-specific management;
  • Assessment of biotic (weeds, disease) stress factors;
  • Assessment of abiotic (nutrition deficiencies, water) stress factors;
  • Crop yield estimation from UAV data;
  • Estimation of crop condition and characterization of management techniques;
  • Integration of UAV data with ground-based datasets;
  • Multi-temporal UAV data for crop development analysis;
  • Multi-temporal UAV data for the analysis of interannual trends and anomalies;
  • 3D crop information.

Dr. Thomas Jarmer
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 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. Sensors 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

  • crop parameter
  • crop yield
  • multisensoral analysis
  • spatial assessment
  • vegetation anomalies
  • biotic and abiotic stress factors
  • crop condition
  • deep learning

Published Papers (3 papers)

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15 pages, 10655 KiB  
Article
Sentinel-2 Data for Precision Agriculture?—A UAV-Based Assessment
by Josephine Bukowiecki, Till Rose and Henning Kage
Sensors 2021, 21(8), 2861; https://doi.org/10.3390/s21082861 - 19 Apr 2021
Cited by 18 | Viewed by 3890
Abstract
An approach of exploiting and assessing the potential of Sentinel-2 data in the context of precision agriculture by using data from an unmanned aerial vehicle (UAV) is presented based on a four-year dataset. An established model for the estimation of the green area [...] Read more.
An approach of exploiting and assessing the potential of Sentinel-2 data in the context of precision agriculture by using data from an unmanned aerial vehicle (UAV) is presented based on a four-year dataset. An established model for the estimation of the green area index (GAI) of winter wheat from a UAV-based multispectral camera was used to calibrate the Sentinel-2 data. Large independent datasets were used for evaluation purposes. Furthermore, the potential of the satellite-based GAI-predictions for crop monitoring and yield prediction was tested. Therefore, the total absorbed photosynthetic radiation between spring and harvest was calculated with satellite and UAV data and correlated with the final grain yield. Yield maps at the same resolution were generated by combining yield data on a plot level with a UAV-based crop coverage map. The best tested model for satellite-based GAI-prediction was obtained by combining the near-, infrared- and Red Edge-waveband in a simple ratio (R2 = 0.82, mean absolute error = 0.52 m2/m2). Yet, the Sentinel-2 data seem to depict average GAI-developments through the seasons, rather than to map site-specific variations at single acquisition dates. The results show that the lower information content of the satellite-based crop monitoring might be mainly traced back to its coarser Red Edge-band. Additionally, date-specific effects within the Sentinel-2 data were detected. Due to cloud coverage, the temporal resolution was found to be unsatisfactory as well. These results emphasize the need for further research on the applicability of the Sentinel-2 data and a cautious use in the context of precision agriculture. Full article
(This article belongs to the Special Issue UAV-Based Remote Sensing Applications in Precision Agriculture)
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22 pages, 6311 KiB  
Article
Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images
by Qian Ma, Wenting Han, Shenjin Huang, Shide Dong, Guang Li and Haipeng Chen
Sensors 2021, 21(6), 1994; https://doi.org/10.3390/s21061994 - 12 Mar 2021
Cited by 13 | Viewed by 2080
Abstract
This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, [...] Read more.
This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, containing three, five, and eight types of crops, respectively. The feature subsets of three study areas are selected by recursive feature elimination (RFE). Object-oriented random forest (OB-RF) and object-oriented support vector machine (OB-SVM) classification models are established for the three study areas. After training the models with the feature subsets, the classification results are evaluated using a confusion matrix. The OB-RF and OB-SVM models’ classification accuracies are 97.09% and 99.13%, respectively, for the low-complexity planting structure. The equivalent values are 92.61% and 99.08% for the medium-complexity planting structure and 88.99% and 97.21% for the high-complexity planting structure. For farmland with fragmentary plots and a high-complexity planting structure, as the planting structure complexity changed from low to high, both models’ overall accuracy levels decreased. The overall accuracy of the OB-RF model decreased by 8.1%, and that of the OB-SVM model only decreased by 1.92%. OB-SVM achieves an overall classification accuracy of 97.21%, and a single-crop extraction accuracy of at least 85.65%. Therefore, UAV multispectral remote sensing can be used for classification applications in highly complex planting structures. Full article
(This article belongs to the Special Issue UAV-Based Remote Sensing Applications in Precision Agriculture)
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18 pages, 9185 KiB  
Letter
Deep Learning Applied to Phenotyping of Biomass in Forages with UAV-Based RGB Imagery
by Wellington Castro, José Marcato Junior, Caio Polidoro, Lucas Prado Osco, Wesley Gonçalves, Lucas Rodrigues, Mateus Santos, Liana Jank, Sanzio Barrios, Cacilda Valle, Rosangela Simeão, Camilo Carromeu, Eloise Silveira, Lúcio André de Castro Jorge and Edson Matsubara
Sensors 2020, 20(17), 4802; https://doi.org/10.3390/s20174802 - 26 Aug 2020
Cited by 54 | Viewed by 4914
Abstract
Monitoring biomass of forages in experimental plots and livestock farms is a time-consuming, expensive, and biased task. Thus, non-destructive, accurate, precise, and quick phenotyping strategies for biomass yield are needed. To promote high-throughput phenotyping in forages, we propose and evaluate the use of [...] Read more.
Monitoring biomass of forages in experimental plots and livestock farms is a time-consuming, expensive, and biased task. Thus, non-destructive, accurate, precise, and quick phenotyping strategies for biomass yield are needed. To promote high-throughput phenotyping in forages, we propose and evaluate the use of deep learning-based methods and UAV (Unmanned Aerial Vehicle)-based RGB images to estimate the value of biomass yield by different genotypes of the forage grass species Panicum maximum Jacq. Experiments were conducted in the Brazilian Cerrado with 110 genotypes with three replications, totaling 330 plots. Two regression models based on Convolutional Neural Networks (CNNs) named AlexNet and ResNet18 were evaluated, and compared to VGGNet—adopted in previous work in the same thematic for other grass species. The predictions returned by the models reached a correlation of 0.88 and a mean absolute error of 12.98% using AlexNet considering pre-training and data augmentation. This proposal may contribute to forage biomass estimation in breeding populations and livestock areas, as well as to reduce the labor in the field. Full article
(This article belongs to the Special Issue UAV-Based Remote Sensing Applications in Precision Agriculture)
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