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RGB Imaging for Crop Monitoring and High-Throughput Plant Phenotyping: Smartphones, Drones and Beyond

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 5615

Special Issue Editors


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Guest Editor
Department of Plant Physiology, Universitat de Barcelona, 08007 Barcelona, Spain
Interests: plant science; crop phenotyping; remote sensing

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Guest Editor
Programa de Ingeniería Electrónica, Facultad de Ingeniería, Universidad de Ibagué, 730001 Ibagué, Colombia
Interests: machine learning; remote sensing; plant phenotyping

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Guest Editor
Plant Sciences Unit, Institute for Agricultural, Fisheries and Food Research (ILVO), 9090 Melle, Belgium AND Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Zwijnaarde, Belgium
Interests: plant phenotyping; molecular genetics; plant breeding

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Guest Editor
Plant Sciences Unit, Institute for Agricultural, Fisheries and Food Research (ILVO), 9090 Melle, Belgium
Interests: high-throughput field phenotyping; precision agriculture; ecophysiology; abiotic stress; machine learning

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Guest Editor
Integrative Crop Ecophysiology Group, Department B.E.E.C.A. Plant Physiology Section, Faculty of Biology, University of Barcelona, Av Diagonal 643, 08028 Barcelona, Spain
Interests: remote sensing; plant ecophysiology; agriculture, forestry; plant phenotyping; spectroscopy and imaging spectroscopy; UAVs; machine learning; data fusion; data processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The use of red, green, and blue (RGB) color imaging can provide a wide range of crop monitoring and plant phenotyping applications, with their utility in application depending basically on the reliability of the algorithms implemented. This is one of the reasons why a low-cost sensor, such as a color digital (RGB) camera, is being included more and more as a key component in the most advanced platforms, whether under controlled or field conditions. In the latter case, RGB cameras can be installed in portable platforms on the ground, from a mobile phone mounted on a pole to sophisticated multisensor platforms, or used aerially (e.g., mounted on unmanned or manned aerial platforms, from drones to aircraft to satellites).

Besides the formulation of vegetation indices, RGB images are amenable for assessing a wide range of other traits, even in the field, such as counting agronomical yield components, assessing phenological stages, conducting regular monitoring of crop development, measuring the growth of individual plants, and identifying foliar symptoms associated to a myriad of biotic and abiotic stresses. This is due to the wide versatility of the data collected by RGB cameras, which is essentially linked to the high resolution of these images and the general high quality of factory color calibration. Other than the appropriate software (to run the specific algorithms), and the use of advanced statistical and modeling approaches, including deep learning, machine learning, and artificial intelligence, which are all developing quickly, the other main limitation is the need for high-performance computing capable of applying results in real-time. Solving that point may pave the way for a wide number of applications to be implemented based on the interpretation and use of RGB images alone.

Prof. Dr. José Luis Araus Ortega
Prof. Dr. Jose A. Fernandez-Gallego
Prof. Dr. Isabel Roldán-Ruiz
Dr. Peter Lootens
Prof. Dr. Shawn C. Kefauver
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

  • Color digital photography
  • RGB (red, green, blue)
  • Crop monitoring
  • Plant phenotyping
  • Functional traits
  • Yield prediction
  • Machine learning
  • Deep learning
  • Artificial intelligence
  • Smartphones
  • Drones
  • Airborne science
  • Satellite
  • Proximal sensing
  • Remote sensing
  • Greenhouse
  • Field research
  • Phenology
  • Biotic stress
  • Abiotic stress
  • Real-time

Published Papers (1 paper)

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Research

15 pages, 43425 KiB  
Article
Remotely Piloted Aircraft and Random Forest in the Evaluation of the Spatial Variability of Foliar Nitrogen in Coffee Crop
by Diego Bedin Marin, Gabriel Araújo e Silva Ferraz, Paulo Henrique Sales Guimarães, Felipe Schwerz, Lucas Santos Santana, Brenon Dienevam Souza Barbosa, Rafael Alexandre Pena Barata, Rafael de Oliveira Faria, Jessica Ellen Lima Dias, Leonardo Conti and Giuseppe Rossi
Remote Sens. 2021, 13(8), 1471; https://doi.org/10.3390/rs13081471 - 10 Apr 2021
Cited by 18 | Viewed by 3530
Abstract
The development of approaches to determine the spatial variability of nitrogen (N) into coffee leaves is essential to increase productivity and reduce production costs and environmental impacts associated with excessive N applications. Thus, this study aimed to assess the potential of the Random [...] Read more.
The development of approaches to determine the spatial variability of nitrogen (N) into coffee leaves is essential to increase productivity and reduce production costs and environmental impacts associated with excessive N applications. Thus, this study aimed to assess the potential of the Random Forest (RF) machine learning method applied to vegetation indices (VI) obtained from Remotely Piloted Aircraft (RPA) images to measure the N content in coffee plants. A total of 10 VI were obtained from multispectral images by a camera attached to a rotary-wing RPA. The RGB orthomosaic was used to determine sampling points at the crop area, which were ranked by N levels in the plants as deficient, critical, or sufficient. The chemical analysis of N content in the coffee leaves, as well as the VI values in sample points, were used as input parameters for the image training and its classification by the RF. The suggested model has shown global accuracy and a kappa coefficient of up to 0.91 and 0.86, respectively. The best results were achieved using the Green Normalized Difference Vegetation (GNDVI) and Green Optimized Soil Adjusted Vegetation Index (GOSAVI). In addition, the model enabled the evaluation of the spatial distribution of N in the coffee trees, as well as quantification of N deficiency in the crop for the whole area. The GNDVI and GOSAVI allowed the verification that 22% of the entire crop area had plants with N deficiency symptoms, which would result in a reduction of 78% in the amount of N applied by the producer. Full article
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