Special Issue "Drones for Precision Agriculture: Remote Sensing Applications"

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: 10 January 2021.

Special Issue Editors

Dr. Javier J. Cancela
Website SciProfiles
Guest Editor
GI-1716, Projects and Planification, Dpto. Ingeniería Agroforestal, Universidad de Santiago de Compostela, Escola Politécnica Superior de Enxeñaría, Rúa Benigno Ledo s/n, 27002 Lugo, Spain
Interests: precision agriculture; crop water requirements; soil–water management; remote sensing
Special Issues and Collections in MDPI journals
Dr. Rocío Ballesteros González
Guest Editor
Crop Production and Agricultural Technology Department, Higher Agricultural and Forestry Engineering School, University of Castilla-La Mancha, Campus Universitario s/n 02051 Albacete, Spain
Interests: precision agriculture; forestry; unmanned aerial vehicles; satellite imagery; irrigation management; soil science; fertility; remote sensing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

It is very well known that based on estimations on future growth, the world population will reach 9100 million by 2050. Therefore, food security should be considered as one of the main targets following the 2030 Sustainable Development Goals. Further, it is not possible to speak about global population increase without considering global warming future scenarios. Water scarcity and droughts will increase overall in those arid areas where water availability is already a problem. Precision agriculture should be considered a useful tool to face all these global challenges. Among those, unmanned aerial vehicles, known as drones, have evolved rapidly in the last decade. Currently, more affordable and easier-to-use drones have found wide use in improving sustainable crop production.

Different sensors, e.g., RGB, multi- and hyperspectral cameras or LIDAR technology, mounted onboard improve sustainable crop production for multiple aspects/applications such as:

  • 3-D maps for soil analysis;
  • Mid-season crop health monitoring;
  • Irrigation equipment monitoring;
  • Pesticide spraying;
  • Increase the yield and overall quality;
  • Wildlife detection.

The continuous development of sensors and drones has generated an increase in the information obtained, requiring the use of more complex data analysis techniques, integrating all available data. Image processing and pattern recognition are required to implement a final application successfully, e.g., using a vegetation index for crop monitoring. Different data analysis techniques for applications with remote sensors need a standardization to advance in the consolidation of drones in precision agriculture.

For all these reasons, basic research studies applied to precision agriculture, in any of the subjects presented, or as a whole, are well received—especially those studies that address data analysis techniques, ‘exportable’ to other applications and crops, facing an ‘open science’. Successful experiences of application of the use of drones in agriculture (woody crops, horticulture, cereals, rice, etc.) based on results of a long-time scale are also expected.

Dr. Javier J. Cancela
Dr. Rocío Ballesteros González
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 2200 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.


  • data processing
  • data analysis techniques
  • soil analysis
  • segmentation
  • monitoring
  • variable rate
  • yield and overall quality

Published Papers (1 paper)

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Open AccessArticle
Potential of UAS-Based Remote Sensing for Estimating Tree Water Status and Yield in Sweet Cherry Trees
Remote Sens. 2020, 12(15), 2359; https://doi.org/10.3390/rs12152359 - 23 Jul 2020
The present work aims to assess the usefulness of five vegetation indices (VI) derived from multispectral UAS imagery to capture the effects of deficit irrigation on the canopy structure of sweet cherry trees (Prunus avium L.) in southeastern Spain. Three irrigation treatments [...] Read more.
The present work aims to assess the usefulness of five vegetation indices (VI) derived from multispectral UAS imagery to capture the effects of deficit irrigation on the canopy structure of sweet cherry trees (Prunus avium L.) in southeastern Spain. Three irrigation treatments were assayed, a control treatment and two regulated deficit irrigation treatments. Four airborne flights were carried out during two consecutive seasons; to compare the results of the remote sensing VI, the conventional and continuous water status indicators commonly used to manage sweet cherry tree irrigation were measured, including midday stem water potential (Ψs) and maximum daily shrinkage (MDS). Simple regression between individual VIs and Ψs or MDS found stronger relationships in postharvest than in preharvest. Thus, the normalized difference vegetation index (NDVI), resulted in the strongest relationship with Ψs (r2 = 0.67) and MDS (r2 = 0.45), followed by the normalized difference red edge (NDRE). The sensitivity analysis identified the optimal soil adjusted vegetation index (OSAVI) as the VI with the highest coefficient of variation in postharvest and the difference vegetation index (DVI) in preharvest. A new index is proposed, the transformed red range vegetation index (TRRVI), which was the only VI able to statistically identify a slight water deficit applied in preharvest. The combination of the VIs studied was used in two machine learning models, decision tree and artificial neural networks, to estimate the extra labor needed for harvesting and the sweet cherry yield. Full article
(This article belongs to the Special Issue Drones for Precision Agriculture: Remote Sensing Applications)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Vigour estimation in vineyards using aerial imagery from Unmanned Aerial Vehicle
Authors: José Ramón Rodríguez Pérez
Affiliation: Universidad de León, Spain
Abstract: This work aims to estimate vigour variables (surface and height of vegetation, weight of pruning wood, etc.), from digital images captured by UAV (Unmanned Aerial Vehicle) with a conventional RGB sensor. Work has been done on two vineyards of the Mencía and Godello varieties located in the Bierzo Designation of Origen (Northwest of Spain). The flights were made with UAV, at low altitude, obtained high spatial resolution images. The cloud of points of the MDE and the orthoimagen, allowed to estimate with precision the height of the plant (H '), the upper area of the vegetation in the vine line (S') and, therefore, the volume occupied by the vegetation (V '). By means of linear adjustment, it has been possible to estimate the weight of pruning wood (MP) with a coefficient of determination R2 = 0.57 (RMSE = 436g). The MP could also be estimated from H’, but the results get worse (R2 = 0.47 and RMSE = 492g). These techniques allow to obtain information on the vigour canopy by a fast, low-cost and non-destructive methodology.

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