Special Issue "Remote Sensing Applications and Agricultural Automation"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Engineering".

Deadline for manuscript submissions: 20 December 2021.

Special Issue Editors

Dr. Dimitrios S. Paraforos
E-Mail Website
Guest Editor
Institute of Agricultural Engineering, University of Hohenheim, Garben Str. 9, 70599 Stuttgart, Germany
Interests: agricultural machinery automation; ISOBUS technologies; unmanned ground and aerial vehicles; decentralized and resilient digital farming systems
Special Issues and Collections in MDPI journals
Dr. Anselme Muzirafuti
E-Mail Website
Guest Editor
Interreg Italia–Malta–Progetto: Pocket Beach Management and Remote Surveillance System, University of Messina, Via F. Stagno d’Alcontres, 31–98166 Messina, Italy
Interests: remote sensing; unmanned aerial vehicles (UAVs); image processing; farming by satellite; geographic information system (GIS); applied geophysics; coastal studies; climate change; land use/cover change; anthropogenic impact; landscape planning; engineering geology; ecological studies
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past few decades, remote sensing technologies have witnessed tremendous improvements in sensors, platforms, and data analysis. Numerous satellite programs provide free or commercial imageries, new unmanned aerial vehicles equipped with high performing sensors are being developed, and a number of wireless sensors and types of machinery have been introduced. However, the applications of these technologies in agricultural engineering are limited and not well documented. Remote sensing technologies are efficient and cost-effective for crop production, crop detection, and crop monitoring. They are used in precision agriculture, digital farming, automation, and robotics. With the ongoing climate change, remote sensing technologies can be used to obtain information on land use/land cover as well as in subsurface. They can be used for point clouds acquisition and 3D reconstruction of crops. Free imageries provided by Sentinel and Landsat satellites can be used for leaf area estimation, field boundary, and cultivated area determination. Their precision and performance can be improved by using very high spatial resolution commercial satellites or orthophotos acquired by UAVs. In addition, navigation satellite system signals can be used in agricultural machinery control, automation, and robotics.

This Special Issue will focus on the latest advances in remote sensing technology applications in agricultural engineering. Authors are invited to submit original manuscripts on topics including (but not limited to): 

  • Photogrammetry for crop detection and crop monitoring;
  • Satellite technologies for farming and precision agriculture;
  • Fusion of remote sensing and applied geophysics for subsurface and hydrogeological modelling;
  • Crop 3D imaging and reconstruction;
  • Multispectral and hyperspectral data analysis;
  • Navigation satellite system signals for agricultural machinery control and site-specific applications;
  • Remote sensing time series data analysis for environmental impact and landscape analyses.

Dr. Dimitrios Paraforos
Dr. Anselme Muzirafuti
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 papers will be 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. Applied Sciences 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 2000 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

  • satellite technologies
  • agricultural engineering
  • precision agricultural
  • automatic control
  • remote sensing
  • unmanned aerial vehicles (UAVs)
  • image processing
  • farming by satellite
  • digital farming
  • geographic information system (GIS)
  • applied geophysics
  • climate change
  • land use/cover change
  • anthropogenic impact
  • landscape planning
  • engineering geology
  • ecological studies
  • 2D imaging, 3D imaging and reconstruction
  • machine learning
  • deep learning
  • autonomous field investigation
  • multispectral data analysis
  • hyperspectral data analysis
  • LIDAR data analysis
  • RADAR data analysis
  • aerial photogrammetry
  • crop production
  • crop detection
  • crop monitoring
  • agricultural machinery control
  • automation and robotics
  • leaf area estimation
  • field boundary and cultivated area determination
  • precision seeding
  • modeling and simulation of water table fluctuation
  • wireless sensor networks
  • drones
  • GPS
  • point clouds acquisition and analysis
  • remote sensing time series data analysis
  • environmental impact

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

Article
Modeling and Spatiotemporal Mapping of Water Quality through Remote Sensing Techniques: A Case Study of the Hassan Addakhil Dam
Appl. Sci. 2021, 11(19), 9297; https://doi.org/10.3390/app11199297 - 07 Oct 2021
Viewed by 613
Abstract
With its high water potential, the Ziz basin is one of the most important basins in Morocco. This paper aims to develop a methodology for spatiotemporal monitoring of the water quality of the Hassan Addakhil dam using remote sensing techniques combined with a [...] Read more.
With its high water potential, the Ziz basin is one of the most important basins in Morocco. This paper aims to develop a methodology for spatiotemporal monitoring of the water quality of the Hassan Addakhil dam using remote sensing techniques combined with a modeling approach. Firstly, several models were established for the different water quality parameters (nitrate, dissolved oxygen and chlorophyll a) by combining field and satellite data. In a second step, the calibration and validation of the selected models were performed based on the following statistical parameters: compliance index R2, the root mean square error and p-value. Finally, the satellite data were used to carry out spatiotemporal monitoring of the water quality. The field results show excellent quality for most of the samples. In terms of the modeling approach, the selected models for the three parameters (nitrate, dissolved oxygen and chlorophyll a) have shown a good correlation between the measured and estimated values with compliance index values of 0.62, 0.56 and 0.58 and root mean square error values of 0.16 mg/L, 0.65 mg/L and 0.07 µg/L for nitrate, dissolved oxygen and chlorophyll a, respectively. After the calibration, the validation and the selection of the models, the spatiotemporal variation of water quality was determined thanks to the multitemporal satellite data. The results show that this approach is an effective and valid methodology for the modeling and spatiotemporal mapping of water quality in the reservoir of the Hassan Addakhil dam. It can also provide valuable support for decision-makers in water quality monitoring as it can be applied to other regions with similar conditions. Full article
(This article belongs to the Special Issue Remote Sensing Applications and Agricultural Automation)
Show Figures

Figure 1

Article
Using Artificial Neural Network Algorithm and Remote Sensing Vegetation Index Improves the Accuracy of the Penman-Monteith Equation to Estimate Cropland Evapotranspiration
Appl. Sci. 2021, 11(18), 8649; https://doi.org/10.3390/app11188649 - 17 Sep 2021
Viewed by 571
Abstract
Accurate estimation of evapotranspiration (ET) can provide useful information for water management and sustainable agricultural development. However, most of the existing studies used physical models, which are not accurate enough due to our limited ability to represent the ET process accurately or rarely [...] Read more.
Accurate estimation of evapotranspiration (ET) can provide useful information for water management and sustainable agricultural development. However, most of the existing studies used physical models, which are not accurate enough due to our limited ability to represent the ET process accurately or rarely focused on cropland. In this study, we trained two models of estimating croplands ET. The first is Medlyn-Penman-Monteith (Medlyn-PM) model. It uses artificial neural network (ANN)-derived gross primary production along with Medlyn’s stomatal conductance to compute surface conductance (Gs), and the computed Gs is used to estimate ET using the PM equation. The second model, termed ANN-PM, directly uses ANN to construct Gs and simulate ET using the PM equation. The results showed that the two models can reasonably reproduce ET with ANN-PM showing a better performance, as indicated by the lower error and higher determination coefficients. The results also showed that the performances of ANN-PM without the facilitation of any remote sensing (RS) factors degraded significantly compared to the versions that used RS factors. We also evidenced that ANN-PM can reasonably characterize the time-series changes of ET at sites having a dry climate. The ANN-PM method can reasonably estimate the ET of croplands under different environmental conditions. Full article
(This article belongs to the Special Issue Remote Sensing Applications and Agricultural Automation)
Show Figures

Figure 1

Article
WebGIS Implementation for Dynamic Mapping and Visualization of Coastal Geospatial Data: A Case Study of BESS Project
Appl. Sci. 2021, 11(17), 8233; https://doi.org/10.3390/app11178233 - 05 Sep 2021
Viewed by 1202
Abstract
Within an E.U.-funded project, BESS (Pocket Beach Management and Remote Surveillance System), the notion of a geographic information system is an indispensable tool for managing the dynamics of georeferenced data and information for any form of territorial planning. This notion was further explored [...] Read more.
Within an E.U.-funded project, BESS (Pocket Beach Management and Remote Surveillance System), the notion of a geographic information system is an indispensable tool for managing the dynamics of georeferenced data and information for any form of territorial planning. This notion was further explored with the creation of a WebGIS portal that will allow local and regional stakeholders/authorities obtain an easy remote access tool to monitor the status of pocket beaches (PB) in the Maltese Archipelago and Sicily. In this paper, we provide a methodological approach for the implementation of a WebGIS necessary for very detailed dynamic mapping and visualization of geospatial coastal data; the description of the dataset necessary for the monitoring of coastal areas, especially the PBs; and a demonstration of a case study for the PBs of Sicily and Malta by using the methodology and the dataset used during the BESS project. Detailed steps involved in the creation of the WebGIS are presented. These include data preparation, data storage, and data publication and transformation into geo-services. With the help of different Open Geospatial Consortium protocols, the WebGIS displays different layers of information for 134 PBs including orthophotos, sedimentological/geomorphological beach characteristics, shoreline evolution, geometric and morphological parameters, shallow water bathymetry, and photographs of pocket beaches. The WebGIS allows not only for identifying, evaluating, and directing potential solutions to present and arising issues, but also enables public access and involvement. It reflects a platform for future local and regional coastal zone monitoring and management, by promoting public/private involvement in addressing coastal issues and providing local public administrations with an improved technology to monitor coastal changes and help better plan suitable interventions. Full article
(This article belongs to the Special Issue Remote Sensing Applications and Agricultural Automation)
Show Figures

Figure 1

Review

Jump to: Research

Review
Anomaly-Based Intrusion Detection Systems in IoT Using Deep Learning: A Systematic Literature Review
Appl. Sci. 2021, 11(18), 8383; https://doi.org/10.3390/app11188383 - 09 Sep 2021
Cited by 1 | Viewed by 645
Abstract
The Internet of Things (IoT) concept has emerged to improve people’s lives by providing a wide range of smart and connected devices and applications in several domains, such as green IoT-based agriculture, smart farming, smart homes, smart transportation, smart health, smart grid, smart [...] Read more.
The Internet of Things (IoT) concept has emerged to improve people’s lives by providing a wide range of smart and connected devices and applications in several domains, such as green IoT-based agriculture, smart farming, smart homes, smart transportation, smart health, smart grid, smart cities, and smart environment. However, IoT devices are at risk of cyber attacks. The use of deep learning techniques has been adequately adopted by researchers as a solution in securing the IoT environment. Deep learning has also successfully been implemented in various fields, proving its superiority in tackling intrusion detection attacks. Due to the limitation of signature-based detection for unknown attacks, the anomaly-based Intrusion Detection System (IDS) gains advantages to detect zero-day attacks. In this paper, a systematic literature review (SLR) is presented to analyze the existing published literature regarding anomaly-based intrusion detection, using deep learning techniques in securing IoT environments. Data from the published studies were retrieved from five databases (IEEE Xplore, Scopus, Web of Science, Science Direct, and MDPI). Out of 2116 identified records, 26 relevant studies were selected to answer the research questions. This review has explored seven deep learning techniques practiced in IoT security, and the results showed their effectiveness in dealing with security challenges in the IoT ecosystem. It is also found that supervised deep learning techniques offer better performance, compared to unsupervised and semi-supervised learning. This analysis provides an insight into how the use of data types and learning methods will affect the performance of deep learning techniques for further contribution to enhancing a novel model for anomaly intrusion detection and prediction. Full article
(This article belongs to the Special Issue Remote Sensing Applications and Agricultural Automation)
Show Figures

Figure 1

Back to TopTop