Special Issue "Deep and Machine Learning Applications in Remote Sensing Data to Monitor and Manage Crops Using Precision Agriculture Systems"

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: 4 May 2022.

Special Issue Editors

Prof. Dr. Carlos Antonio Da Silva Junior
E-Mail Website
Guest Editor
Department of Geography, Universidade do Estado do Mato Grosso (UNEMAT), Sinop 78555-000, Brazil
Interests: remote sensing applied to environmental monitoring; remote sensing for sustainable agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Luciano Shozo Shiratsuchi
E-Mail Website
Guest Editor
School of Plant, Enviromental and Soil Sciences, Louisiana State University (LSU), Baton Rouge, LA 70808, USA
Interests: precision agriculture; remote sensing; on-farm precision experimentation

Special Issue Information

Dear Colleagues,

With the evolution of orbital and proximal remote sensing technologies, a big data that must be converted to information is being generated in the agricultural sector. These data when analyzed with machine and deep learning approaches applied to remote sensing products have been recently used with success. The computational power using cloud based systems and recent advances on farm machinery equipments providing data collection, processing and analysis open up several opportunities of development and adoption of new technologies. Large scale on farm precision experimentation conducted in partnership with commercial farms and the appearence of new sensors on board of UAVs, crop duster airplanes and satelittes such as radar technologies that allow daily remote data collection under cloudy skies are exciting and require more investigation of several sorts. New equipment, sensors are enabling a better crop monitoring and land use map as well in a regional scale. The intent of this topical edition of Remote Sensing is to convey publications from collaborators that are working with a big pool of data that is being analyzed using deep and machine learning approaches in Precision Agriculture and also to improve regional scale remote sensing applications.

Prof. Dr. Carlos Antonio Da Silva Junior
Dr. Luciano Shiratsuchi
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. 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 2400 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
  • Active crop canopy sensors
  • On farm precision experimentation
  • Monitoring crop areas
  • Neural network
  • Image processing
  • Orbital sensors

Published Papers (2 papers)

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Research

Article
Identification of Infiltration Features and Hydraulic Properties of Soils Based on Crop Water Stress Derived from Remotely Sensed Data
Remote Sens. 2021, 13(20), 4127; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13204127 - 15 Oct 2021
Viewed by 428
Abstract
Knowledge of the spatial variability of soil hydraulic properties is important for many reasons, e.g., for soil erosion protection, or the assessment of surface and subsurface runoff. Nowadays, precision agriculture is gaining importance for which knowledge of soil hydraulic properties is essential, especially [...] Read more.
Knowledge of the spatial variability of soil hydraulic properties is important for many reasons, e.g., for soil erosion protection, or the assessment of surface and subsurface runoff. Nowadays, precision agriculture is gaining importance for which knowledge of soil hydraulic properties is essential, especially when it comes to the optimization of nitrogen fertilization. The present work aimed to exploit the ability of vegetation cover to identify the spatial variability of soil hydraulic properties through the expression of water stress. The assessment of the spatial distribution of saturated soil hydraulic conductivity (Ks) and field water capacity (FWC) was based on a combination of ground-based measurements and thermal and hyperspectral airborne imaging data. The crop water stress index (CWSI) was used as an indicator of crop water stress to assess the hydraulic properties of the soil. Supplementary vegetation indices were used. The support vector regression (SVR) method was used to estimate soil hydraulic properties from aerial data. Data analysis showed that the approach estimated Ks with good results (R2 = 0.77) for stands with developed crop water stress. The regression coefficient values for estimation of FWC for topsoil (0–0.3 m) ranged from R2 = 0.38 to R2 = 0.99. The differences within the study sites of the FWC estimations were higher for the subsoil layer (0.3–0.6 m). R2 values ranged from 0.12 to 0.99. Several factors affect the quality of the soil hydraulic features estimation, such as crop water stress development, condition of the crops, period and time of imaging, etc. The above approach is useful for practical applications for its relative simplicity, especially in precision agriculture. Full article
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Article
Crop Classification of Satellite Imagery Using Synthetic Multitemporal and Multispectral Images in Convolutional Neural Networks
Remote Sens. 2021, 13(17), 3378; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173378 - 25 Aug 2021
Viewed by 739
Abstract
The demand for new tools for mass remote sensing of crops, combined with the open and free availability of satellite imagery, has prompted the development of new methods for crop classification. Because this classification is frequently required to be completed within a specific [...] Read more.
The demand for new tools for mass remote sensing of crops, combined with the open and free availability of satellite imagery, has prompted the development of new methods for crop classification. Because this classification is frequently required to be completed within a specific time frame, performance is also essential. In this work, we propose a new method that creates synthetic images by extracting satellite data at the pixel level, processing all available bands, as well as their data distributed over time considering images from multiple dates. With this approach, data from images of Sentinel-2 are used by a deep convolutional network system, which will extract the necessary information to discern between different types of crops over a year after being trained with data from previous years. Following the proposed methodology, it is possible to classify crops and distinguish between several crop classes while also being computationally low-cost. A software system that implements this method has been used in an area of Extremadura (Spain) as a complementary monitoring tool for the subsidies supported by the Common Agricultural Policy of the European Union. Full article
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