Special Issue "Remote Sensing and Decision Support for Precision Orchard Production"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editors

Prof. Guijun Yang
E-Mail Website
Guest Editor
National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing Research Center for Information Technology in Agriculture Beijing, Academy of Agriculture and Forestry Sciences, 11 Middle Road, Haidian District, Beijing 100097, China
Tel. +86 (0)10 5150 3647
Interests: remote sensing; agronomic modelling; UAV-based sensors; precision farming
Special Issues and Collections in MDPI journals
Dr. James Taylor
E-Mail Website
Guest Editor
National Research Institute of Science and Technology for Environment and Agriculture, UMR ITAP, Montpellier, Languedoc-Roussillon, France
Interests: precision farming; crop management; viticulture; spatial statistics; soil mapping
Prof. Raffaele Casa
E-Mail Website
Guest Editor
Department of Agricultural and Forestry scieNcEs (DAFNE), Tuscia University Via San Camillo de Lellis, 01100 Viterbo, Italy
Tel. +390761357555
Interests: precision agriculture; remote sensing; agronomic modelling; data assimilation
Special Issues and Collections in MDPI journals
Dr. Hao Yang
E-Mail Website
Guest Editor
National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences,11 Middle Road, Haidian District, Beijing 100097, China
Tel. +86 (0)10 5150 3215
Interests: LiDAR; synthetic aperture radar (SAR); precision agriculture; crop phenotyping

Special Issue Information

Dear Colleagues,

Smart orchard is a new generation of orchard production system relying on smart sensing, smart decision, and smart intervention, targeted to optimize agronomic inputs and resilience, and improve yield/quality and production efficiency. The evolution of remote sensing techniques has opened new perspectives for supporting orchard production and management. The enhanced spectral, spatial, and temporal resolution of various sensors (i.e., multi/hyperspectral, LiDAR, thermal, and fluorescence,) on board platforms (spaceborne, airborne, UAVs, vehicle, robots, and backpack) offers unprecedented possibilities for efficient orchard monitoring at different application scales and purposes. The link of the remote sensing technology and orchard agronomic model is expected to support intelligent decisions regarding fertilizer, water, and chemical inputs, optimizing and predicting fruit yield and quality. To accelerate the transition from traditional orchard production to smart orchard, this Special Issue aims at providing the state-of-the-art of remote sensing techniques for orchard management, with a special focus on operational applications targeted to the needs of the final users, that is, fruit producers, farmer associations, and regulating authorities.

This Special Issue invites contributions on the following:

  • Innovative sensors and technologies for smart orchard sensing;
  • Novel intelligent data fusion methods for orchard understanding and learning;
  • Orchard modelling and decision system in smart orchard application.

Submissions are encouraged to cover a broad range of topics that may include, but are not limited to, the following activities:

  • Plant healthy sensor development IoT for orchard management
  • Multi-platform data fusion on smart orchard
  • Empirical and physical model of remote sensing
  • Quantitative inversion of orchard physicochemical parameters
  • Orchard structure parameters estimation
  • Nutrition/water stress diagnosis
  • Pest/disease monitoring and prediction orchard process-based agronomic model
  • Intelligent decision-making tools
  • Traceability system based on smart sensing
  • Orchard yield and quality estimation
  • Remote sensing and data assimilation
  • Big data and systems for smart orchard
  • Social–economical assessment for sensing technology

Prof. Guijun Yang
Prof. Zhenhong Li
Dr. James Taylor
Prof. Raffaele Casa
Dr. Hao Yang
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 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 UAV–UGV remote sensing 
  • Multi-spectral/hyper-spectral 
  • LiDAR/TLS 
  • Thermal
  • Data fusion 
  • Deep learning 
  • Artificial intelligence (AI) 
  • Nutrition stress 
  • Water stress
  • Pest and diseases
  • Yield mapping and prediction 
  • Fruit quality prediction 
  • Orchard pruning
  • Orchard 3D model
  • Process-based crop modeling
  • Structural–functional model
  • Radiative transfer model
  • Plant growth and health
  • Orchard decision making 
  • Multi-GNSS 
  • Precision orchard spraying
  • Traceability system 
  • Social–economical assessment

Published Papers (1 paper)

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Research

Open AccessArticle
Extraction of Information about Individual Trees from High-Spatial-Resolution UAV-Acquired Images of an Orchard
Remote Sens. 2020, 12(1), 133; https://doi.org/10.3390/rs12010133 - 01 Jan 2020
Abstract
The extraction of information about individual trees is essential to supporting the growing of fruit in orchard management. Data acquired from spectral sensors mounted on unmanned aerial vehicles (UAVs) have very high spatial and temporal resolution. However, an efficient and reliable method for [...] Read more.
The extraction of information about individual trees is essential to supporting the growing of fruit in orchard management. Data acquired from spectral sensors mounted on unmanned aerial vehicles (UAVs) have very high spatial and temporal resolution. However, an efficient and reliable method for extracting information about individual trees with irregular tree-crown shapes and a complicated background is lacking. In this study, we developed and tested the performance of an approach, based on UAV imagery, to extracting information about individual trees in an orchard with a complicated background that includes apple trees (Plot 1) and pear trees (Plot 2). The workflow involves the construction of a digital orthophoto map (DOM), digital surface models (DSMs), and digital terrain models (DTMs) using the Structure from Motion (SfM) and Multi-View Stereo (MVS) approaches, as well as the calculation of the Excess Green minus Excess Red Index (ExGR) and the selection of various thresholds. Furthermore, a local-maxima filter method and marker-controlled watershed segmentation were used for the detection and delineation, respectively, of individual trees. The accuracy of the proposed method was evaluated by comparing its results with manual estimates of the numbers of trees and the areas and diameters of tree-crowns, all three of which parameters were obtained from the DOM. The results of the proposed method are in good agreement with these manual estimates: The F-scores for the estimated numbers of individual trees were 99.0% and 99.3% in Plot 1 and Plot 2, respectively, while the Producer’s Accuracy (PA) and User’s Accuracy (UA) for the delineation of individual tree-crowns were above 95% for both of the plots. For the area of individual tree-crowns, root-mean-square error (RMSE) values of 0.72 m2 and 0.48 m2 were obtained for Plot 1 and Plot 2, respectively, while for the diameter of individual tree-crowns, RMSE values of 0.39 m and 0.26 m were obtained for Plot 1 (339 trees correctly identified) and Plot 2 (203 trees correctly identified), respectively. Both the areas and diameters of individual tree-crowns were overestimated to varying degrees. Full article
(This article belongs to the Special Issue Remote Sensing and Decision Support for Precision Orchard Production)
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