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Agronomy
Special Issues
Application of Remote Sensing in Orchard Management
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Application of Remote Sensing in Orchard Management

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Innovative Cropping Systems".

Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 18289

Special Issue Editor

Dr. Laurent Tits

E-Mail Website
Guest Editor
Remote sensing department, Flemish institute for Technological research (VITO), 2400 Mol, Belgium
Interests: remote sensing; precision agriculture; agricultural development; water productivity; sustainable development goals

Special Issue Information

Dear Colleagues,

In precision farming, timely management decisions rely on accurate, easily available, and detailed information about crop vitality and productivity. Remote sensing is more and more being considered a reliable information source, with many operational applications emerging to assist famers in their daily management. However, these applications are mainly focused on annual, monoculture crops. For perennials such as fruit orchards, extracting the required information from remote sensing data sources has proven to be much more difficult. These difficulties can be attributed to a variety of reasons, such as the influence of the soil/grass background due to the sparse coverage of the trees; the complexity of the tree structure, which results in more undesired effects such as variable illumination conditions; and the carry-over effects of stresses and diseases over multiple growing seasons.

This Special Issue will focus on new and innovative solutions that have been developed in order to overcome these issues, and will help make remote sensing a useful and indispensable tool to inform farmers on the vitality and productivity of the trees in fruit orchards, and/or on the variability in soil properties in order to better understand within-field variability. Submissions should be in line with the journal's scope.

Dr. Laurent Tits
Guest Editor

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. Agronomy is an international peer-reviewed open access monthly 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 2600 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

  • remote sensing
  • orchard management
  • precision farming
  • disease monitoring
  • stress detection
  • data fusion
  • time series analysis
  • tree vitality
  • tree productivity
  • soil mapping

Published Papers (4 papers)

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Research

12 pages, 1808 KiB  
Article
Fire Blight Monitoring in Pear Orchards by Unmanned Airborne Vehicles (UAV) Systems Carrying Spectral Sensors
by Hilde Schoofs, Stephanie Delalieux, Tom Deckers and Dany Bylemans
Agronomy 2020, 10(5), 615; https://doi.org/10.3390/agronomy10050615 - 25 Apr 2020
Cited by 16 | Viewed by 3225
Abstract
Controlling fire blight in pear production areas depends strongly on regular visual inspections of pome fruit orchards, nurseries and other hosts of Erwinia amylovora. In addition, these inspections play an essential role in delineating fire blight free production areas, which has important [...] Read more.
Controlling fire blight in pear production areas depends strongly on regular visual inspections of pome fruit orchards, nurseries and other hosts of Erwinia amylovora. In addition, these inspections play an essential role in delineating fire blight free production areas, which has important implications for fruit export. However, visual monitoring is labor intensive and time consuming. As a potential alternative, the performance of spectral sensors on unmanned airborne vehicles (UAV) or drones was evaluated, since this allows the monitoring of larger areas compared to the current field inspections. Unlike more traditional remote sensing platforms such as manned aircrafts and satellites, UAVs offer a higher flexibility and an extremely high level of detail. In this project, a UAV platform carrying a hyperspectral COSI-cam camera was used to map a heavily infected pear orchard. The hyperspectral data were used to assess which wavebands contain information on fire blight infections. In this study, wavelengths 611 nm and 784 nm were found appropriate to detect symptoms associated with fire blight. Vegetation indices that allow to discriminate between healthy and infected trees were identified, too. This manuscript highlights the potential use of the UAV methodology in fire blight detection and remaining difficulties that still need to be overcome for the technique to become fully operational in practice. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Orchard Management)
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26 pages, 4943 KiB  
Article
Pear Flower Cluster Quantification Using RGB Drone Imagery
by Yasmin Vanbrabant, Stephanie Delalieux, Laurent Tits, Klaas Pauly, Joke Vandermaesen and Ben Somers
Agronomy 2020, 10(3), 407; https://doi.org/10.3390/agronomy10030407 - 17 Mar 2020
Cited by 24 | Viewed by 5969
Abstract
High quality fruit production requires the regulation of the crop load on fruit trees by reducing the number of flowers and fruitlets early in the growing season, if the bearing is too high. Several automated flower cluster quantification methods based on proximal and [...] Read more.
High quality fruit production requires the regulation of the crop load on fruit trees by reducing the number of flowers and fruitlets early in the growing season, if the bearing is too high. Several automated flower cluster quantification methods based on proximal and remote imagery methods have been proposed to estimate flower cluster numbers, but their overall performance is still far from satisfactory. For other methods, the performance of the method to estimate flower clusters within a tree is unknown since they were only tested on images from one perspective. One of the main reported bottlenecks is the presence of occluded flowers due to limitations of the top-view perspective of the platform-sensor combinations. In order to tackle this problem, the multi-view perspective from the Red–Green–Blue (RGB) colored dense point clouds retrieved from drone imagery are compared and evaluated against the field-based flower cluster number per tree. Experimental results obtained on a dataset of two pear tree orchards (N = 144) demonstrate that our 3D object-based method, a combination of pixel-based classification with the stochastic gradient boosting algorithm and density-based clustering (DBSCAN), significantly outperforms the state-of-the-art in flower cluster estimations from the 2D top-view (R2 = 0.53), with R2 > 0.7 and RRMSE < 15%. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Orchard Management)
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17 pages, 8971 KiB  
Article
Remote Measurement of Apple Orchard Canopy Information Using Unmanned Aerial Vehicle Photogrammetry
by Guoxiang Sun, Xiaochan Wang, Yongqian Ding, Wei Lu and Ye Sun
Agronomy 2019, 9(11), 774; https://doi.org/10.3390/agronomy9110774 - 19 Nov 2019
Cited by 22 | Viewed by 4590
Abstract
Information on fruit tree canopies is important for decision making in orchard management, including irrigation, fertilization, spraying, and pruning. An unmanned aerial vehicle (UAV) imaging system was used to establish an orchard three-dimensional (3D) point-cloud model. A row-column detection method was developed based [...] Read more.
Information on fruit tree canopies is important for decision making in orchard management, including irrigation, fertilization, spraying, and pruning. An unmanned aerial vehicle (UAV) imaging system was used to establish an orchard three-dimensional (3D) point-cloud model. A row-column detection method was developed based on the probability density estimation and rapid segmentation of the point-cloud data for each apple tree, through which the tree canopy height, H, width, W, and volume, V, were determined for remote orchard canopy evaluation. When the ground sampling distance (GSD) was in the range of 2.13 to 6.69 cm/px, the orchard point-cloud model had a measurement accuracy of 100.00% for the rows and 90.86% to 98.20% for the columns. The coefficient of determination, R2, was in the range of 0.8497 to 0.9376, 0.8103 to 0.9492, and 0.8032 to 0.9148, respectively, and the average relative error was in the range of 1.72% to 3.42%, 2.18% to 4.92%, and 7.90% to 13.69%, respectively, among the H, W, and V values measured manually and by UAV photogrammetry. The results showed that UAV visual imaging is suitable for 3D morphological remote canopy evaluations, facilitates orchard canopy informatization, and contributes substantially to efficient management and control of modern standard orchards. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Orchard Management)
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12 pages, 4710 KiB  
Article
Three-Dimensional Morphological Measurement Method for a Fruit Tree Canopy Based on Kinect Sensor Self-Calibration
by Haihui Yang, Xiaochan Wang and Guoxiang Sun
Agronomy 2019, 9(11), 741; https://doi.org/10.3390/agronomy9110741 - 11 Nov 2019
Cited by 16 | Viewed by 3811
Abstract
Perception of the fruit tree canopy is a vital technology for the intelligent control of a modern standardized orchard. Due to the complex three-dimensional (3D) structure of the fruit tree canopy, morphological parameters extracted from two-dimensional (2D) or single-perspective 3D images are not [...] Read more.
Perception of the fruit tree canopy is a vital technology for the intelligent control of a modern standardized orchard. Due to the complex three-dimensional (3D) structure of the fruit tree canopy, morphological parameters extracted from two-dimensional (2D) or single-perspective 3D images are not comprehensive enough. Three-dimensional information from different perspectives must be combined in order to perceive the canopy information efficiently and accurately in complex orchard field environment. The algorithms used for the registration and fusion of data from different perspectives and the subsequent extraction of fruit tree canopy related parameters are the keys to the problem. This study proposed a 3D morphological measurement method for a fruit tree canopy based on Kinect sensor self-calibration, including 3D point cloud generation, point cloud registration and canopy information extraction of apple tree canopy. Using 32 apple trees (Yanfu 3 variety) morphological parameters of the height (H), maximum canopy width (W) and canopy thickness (D) were calculated. The accuracy and applicability of this method for extraction of morphological parameters were statistically analyzed. The results showed that, on both sides of the fruit trees, the average relative error (ARE) values of the morphological parameters including the fruit tree height (H), maximum tree width (W) and canopy thickness (D) between the calculated values and measured values were 3.8%, 12.7% and 5.0%, respectively, under the V1 mode; the ARE values under the V2 mode were 3.3%, 9.5% and 4.9%, respectively; and the ARE values under the V1 and V2 merged mode were 2.5%, 3.6% and 3.2%, respectively. The measurement accuracy of the tree width (W) under the double visual angle mode had a significant advantage over that under the single visual angle mode. The 3D point cloud reconstruction method based on Kinect self-calibration proposed in this study has high precision and stable performance, and the auxiliary calibration objects are readily portable and easy to install. It can be applied to different experimental scenes to extract 3D information of fruit tree canopies and has important implications to achieve the intelligent control of standardized orchards. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Orchard Management)
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