Special Issue "UAV/Drones for Agriculture and Forestry"

A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (28 February 2019).

Special Issue Editors

Dr. Harm Bartholomeus
Website
Guest Editor
Laboratory of Geo-information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 3, 6708PB Wageningen, The Netherlands
Interests: Ecology; UAV; Geography; Land degradation; Remote sensing; 3d analysis; Erosion; Geomorphology; Soil; Spectroscopy; Terrestrial laser scanning
Dr. Lammert Kooistra
Website
Guest Editor
Laboratory of Geo-information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 3, 6708PB Wageningen, The Netherlands
Interests: image spectroscopy; unmanned aerial vehicle; agronomy; sensor integration; machine learning
Special Issues and Collections in MDPI journals
Dr. Javier J. Cancela
Website
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
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Dr. Xesús P. González
Website
Guest Editor
Agroforestry Engineering Department. University of Santiago de Compostela
Interests: precision agriculture; neuronal networks; software implementation; remote sensing; geographic information systems
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Prof. Dr. Francisco Javier Mesas Carrascosa
Website
Guest Editor
Department of Graphic Engineering and Geomatics, Higher Technical School of Agricultural Engineering, University of Córdoba , 14071, Spain
Interests: UAV; remote sensing; precision agriculture; precision forestry; Geomatic
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

UAV technology is developing fast in terms of platforms, cameras and integrated systems. Increasingly, commercial systems allow fast and reliable access to this technology. Agriculture and forestry are seen as important domains that could benefit from the added value of flexibility and increased spatial resolution of UAVs. However, the processing of the raw UAV datasets towards tailor-made end-products and services is still a critical step.

Although remote sensing methods for vegetation and crop analysis have already been developed for decades from satellite-based images, the specific characteristics of data derived from on-board UAV sensors allows for new methods to be developed and alternative products to be created. Taking advantage of the increased spatial resolution of the images provides opportunities for machine vision approaches developed in other domains. Simultaneously deriving spectral and height information using either multispectral sensors alone or in combination with LiDAR sensors allows machine learning approaches to improve the retrieval of relevant plant traits.

This Special Issue focuses on innovative approaches in the processing chain of UAV acquired data for applications in agriculture and forestry, including, but not limited to, the following topics:

  • Calibration and modelling sensors
  • Methodologies to extract information at feature level from images
  • Object recognition and machine vision
  • Time series and change analysis methods
  • Real time exploration
  • Retrieval of plant traits including 3D measurements
  • Sensor-based decision support systems  
  • Mechatronics, robotics 
  • Integration of UAVs with other systems/platforms; this is relevant when linking terrestrial sensors, UAV and/or satellite images when studying large areas such as forests
  • Specific agricultural applications such as fertilization management, pest management, weed detection, disease detection, mapping of plant health, ...
  • Specific forestry applications such as mapping of forest biomass, species identification, forest structure, biochemistry, ...

Dr. Harm Bartholomeus
Dr. Lammert Kooistra
Dr. Javier J. Cancela
Dr. Xesús P. González
Dr. Francisco Javier Mesas Carrascosa
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. Drones is an international peer-reviewed open access quarterly 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 1000 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.

Published Papers (5 papers)

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Research

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Open AccessArticle
Using Unmanned Aerial Systems (UAS) and Object-Based Image Analysis (OBIA) for Measuring Plant-Soil Feedback Effects on Crop Productivity
Drones 2019, 3(3), 54; https://doi.org/10.3390/drones3030054 - 30 Jun 2019
Cited by 2
Abstract
Unmanned aerial system (UAS) acquired high-resolution optical imagery and object-based image analysis (OBIA) techniques have the potential to provide spatial crop productivity information. In general, plant-soil feedback (PSF) field studies are time-consuming and laborious which constrain the scale at which these studies can [...] Read more.
Unmanned aerial system (UAS) acquired high-resolution optical imagery and object-based image analysis (OBIA) techniques have the potential to provide spatial crop productivity information. In general, plant-soil feedback (PSF) field studies are time-consuming and laborious which constrain the scale at which these studies can be performed. Development of non-destructive methodologies is needed to enable research under actual field conditions and at realistic spatial and temporal scales. In this study, the influence of six winter cover crop (WCC) treatments (monocultures Raphanus sativus, Lolium perenne, Trifolium repens, Vicia sativa and two species mixtures) on the productivity of succeeding endive (Cichorium endivia) summer crop was investigated by estimating crop volume. A three-dimensional surface and terrain model were photogrammetrically reconstructed from UAS imagery, acquired on 1 July 2015 in Wageningen, the Netherlands. Multi-resolution image segmentation (MIRS) and template matching algorithms were used in an integrated workflow to detect individual crops (accuracy = 99.8%) and delineate C. endivia crop covered area (accuracy = 85.4%). Mean crop area (R = 0.61) and crop volume (R = 0.71) estimates had strong positive correlations with in situ measured dry biomass. Productivity differences resulting from the WCC treatments were greater for estimated crop volume in comparison to in situ biomass, the legacy of Raphanus was most beneficial for estimated crop volume. The perennial ryegrass L. perenne treatment resulted in a significantly lower production of C. endivia. The developed workflow has potential for PSF studies as well as precision farming due to its flexibility and scalability. Our findings provide insight into the potential of UAS for determining crop productivity on a large scale. Full article
(This article belongs to the Special Issue UAV/Drones for Agriculture and Forestry)
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Open AccessArticle
Assessment of Texture Features for Bermudagrass (Cynodon dactylon) Detection in Sugarcane Plantations
Drones 2019, 3(2), 36; https://doi.org/10.3390/drones3020036 - 13 Apr 2019
Cited by 1
Abstract
Sugarcane products contribute significantly to the Brazilian economy, generating U.S. $12.2 billion in revenue in 2018. Identifying and monitoring factors that induce yield reduction, such as weed occurrence, is thus imperative. The detection of Bermudagrass in sugarcane crops using remote sensing data, however, [...] Read more.
Sugarcane products contribute significantly to the Brazilian economy, generating U.S. $12.2 billion in revenue in 2018. Identifying and monitoring factors that induce yield reduction, such as weed occurrence, is thus imperative. The detection of Bermudagrass in sugarcane crops using remote sensing data, however, is a challenge considering their spectral similarity. To overcome this limitation, this paper aims to explore the potential of texture features derived from images acquired by an optical sensor onboard anunmanned aerial vehicle (UAV) to detect Bermudagrass in sugarcane. Aerial images with a spatial resolution of 2 cm were acquired from a sugarcane field in Brazil. The Green-Red Vegetation Index and several texture metrics derived from the gray-level co-occurrence matrix were calculated to perform an automatic classification using arandom forest algorithm. Adding texture metrics to the classification process improved the overall accuracy from 83.00% to 92.54%, and this improvement was greater considering larger window sizes, since they representeda texture transition between two targets. Production losses induced by Bermudagrass presence reached 12.1 tons × ha−1 in the study site. This study not only demonstrated the capacity of UAV images to overcome the well-known limitation of detecting Bermudagrass in sugarcane crops, but also highlighted the importance of texture for high-accuracy quantification of weed invasion in sugarcane crops. Full article
(This article belongs to the Special Issue UAV/Drones for Agriculture and Forestry)
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Open AccessArticle
Identification of Ramularia Leaf Blight Cotton Disease Infection Levels by Multispectral, Multiscale UAV Imagery
Drones 2019, 3(2), 33; https://doi.org/10.3390/drones3020033 - 02 Apr 2019
Cited by 3
Abstract
The reduction of the production cost and negative environmental impacts by pesticide application to control cotton diseases depends on the infection patterns spatialized in the farm scale. Here, we evaluate the potential of three-band multispectral imagery from a multi-rotor unmanned airborne vehicle (UAV) [...] Read more.
The reduction of the production cost and negative environmental impacts by pesticide application to control cotton diseases depends on the infection patterns spatialized in the farm scale. Here, we evaluate the potential of three-band multispectral imagery from a multi-rotor unmanned airborne vehicle (UAV) platform for the detection of ramularia leaf blight from different flight heights in an experimental field. Increasing infection levels indicate the progressive degradation of the spectral vegetation signal, however, they were not sufficient to differentiate disease severity levels. At resolutions of ~5 cm (100 m) and ~15 cm (300 m) up to a ground spatial resolution of ~25 cm (500 m flight height), two-scaled infection levels can be detected for the best performing algorithm of four classifiers tested, with an overall accuracy of ~79% and a kappa index of ~0.51. Despite limited classification performance, the results show the potential interest of low-cost multispectral systems to monitor ramularia blight in cotton. Full article
(This article belongs to the Special Issue UAV/Drones for Agriculture and Forestry)
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Open AccessArticle
Multispectral, Aerial Disease Detection for Myrtle Rust (Austropuccinia psidii) on a Lemon Myrtle Plantation
Drones 2019, 3(1), 25; https://doi.org/10.3390/drones3010025 - 07 Mar 2019
Cited by 2
Abstract
Disease management in agriculture often assumes that pathogens are spread homogeneously across crops. In practice, pathogens can manifest in patches. Currently, disease detection is predominantly carried out by human assessors, which can be slow and expensive. A remote sensing approach holds promise. Current [...] Read more.
Disease management in agriculture often assumes that pathogens are spread homogeneously across crops. In practice, pathogens can manifest in patches. Currently, disease detection is predominantly carried out by human assessors, which can be slow and expensive. A remote sensing approach holds promise. Current satellite sensors are not suitable to spatially resolve individual plants or lack temporal resolution to monitor pathogenesis. Here, we used multispectral imaging and unmanned aerial systems (UAS) to explore whether myrtle rust (Austropuccinia psidii) could be detected on a lemon myrtle (Backhousia citriodora) plantation. Multispectral aerial imagery was collected from fungicide treated and untreated tree canopies, the fungicide being used to control myrtle rust. Spectral vegetation indices and single spectral bands were used to train a random forest classifier. Treated and untreated trees could be classified with high accuracy (95%). Important predictors for the classifier were the near-infrared (NIR) and red edge (RE) spectral band. Taking some limitations into account, that are discussedherein, our work suggests potential for mapping myrtle rust-related symptoms from aerial multispectral images. Similar studies could focus on pinpointing disease hotspots to adjust management strategies and to feed epidemiological models. Full article
(This article belongs to the Special Issue UAV/Drones for Agriculture and Forestry)
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Review

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Open AccessReview
A Review on the Use of Unmanned Aerial Vehicles and Imaging Sensors for Monitoring and Assessing Plant Stresses
Drones 2019, 3(2), 40; https://doi.org/10.3390/drones3020040 - 20 Apr 2019
Cited by 15
Abstract
Unmanned aerial vehicles (UAVs) are becoming a valuable tool to collect data in a variety of contexts. Their use in agriculture is particularly suitable, as those areas are often vast, making ground scouting difficult, and sparsely populated, which means that injury and privacy [...] Read more.
Unmanned aerial vehicles (UAVs) are becoming a valuable tool to collect data in a variety of contexts. Their use in agriculture is particularly suitable, as those areas are often vast, making ground scouting difficult, and sparsely populated, which means that injury and privacy risks are not as important as in urban settings. Indeed, the use of UAVs for monitoring and assessing crops, orchards, and forests has been growing steadily during the last decade, especially for the management of stresses such as water, diseases, nutrition deficiencies, and pests. This article presents a critical overview of the main advancements on the subject, focusing on the strategies that have been used to extract the information contained in the images captured during the flights. Based on the information found in more than 100 published articles and on our own research, a discussion is provided regarding the challenges that have already been overcome and the main research gaps that still remain, together with some suggestions for future research. Full article
(This article belongs to the Special Issue UAV/Drones for Agriculture and Forestry)
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