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

Guest Editor
Dr. Harm Bartholomeus

Laboratory of Geo-information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 3, 6708PB Wageningen, The Netherlands
Website | E-Mail
Interests: Ecology; UAV; Geography; Land degradation; Remote sensing; 3d analysis; Erosion; Geomorphology; Soil; Spectroscopy; Terrestrial laser scanning
Guest Editor
Dr. Lammert Kooistra

Laboratory of Geo-information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 3, 6708PB Wageningen, The Netherlands
Website | E-Mail
Interests: image spectroscopy; unmanned aerial vehicle; agronomy; sensor integration; machine learning
Guest Editor
Dr. Javier J. Cancela

Agroforestry Engineering Department. University of Santiago de Compostela
Website | E-Mail
Interests: fertigation; irrigation; crop water requirements; soil–water balance; precision agriculture; integrated water management
Guest Editor
Dr. Xesús P. González

Agroforestry Engineering Department. University of Santiago de Compostela
Website | E-Mail
Interests: precision agriculture; neuronal networks; software implementation; remote sensing; geographic information systems
Guest Editor
Dr. Francisco Javier Mesas Carrascosa

Department of Graphic Engineering and Geomatics, University of Cordoba, Campus de Rabanales, 14071 Cordoba, Spain
Website | E-Mail
Interests: UAV; LiDAR; agriculture; forestry; GIS

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) is waived for well-prepared manuscripts submitted to this issue. 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 (4 papers)

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Research

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Open AccessArticle
Assessment of Texture Features for Bermudagrass (Cynodon dactylon) Detection in Sugarcane Plantations
Received: 27 February 2019 / Revised: 5 April 2019 / Accepted: 10 April 2019 / Published: 13 April 2019
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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
Received: 19 February 2019 / Revised: 27 March 2019 / Accepted: 1 April 2019 / Published: 2 April 2019
PDF Full-text (2437 KB) | HTML Full-text | XML Full-text
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
Received: 1 February 2019 / Revised: 26 February 2019 / Accepted: 1 March 2019 / Published: 7 March 2019
PDF Full-text (2920 KB) | HTML Full-text | XML Full-text | Supplementary Files
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

Jump to: Research

Open AccessReview
A Review on the Use of Unmanned Aerial Vehicles and Imaging Sensors for Monitoring and Assessing Plant Stresses
Received: 29 March 2019 / Revised: 12 April 2019 / Accepted: 17 April 2019 / Published: 20 April 2019
Cited by 1 | PDF Full-text (409 KB) | HTML Full-text | XML Full-text
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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