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Drones
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Precision Agriculture, Horticulture and Forestry: Extracting Canopy Information from Drone...
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Precision Agriculture, Horticulture and Forestry: Extracting Canopy Information from Drone Imagery for Management and Decision-Making

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

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 26753

Special Issue Editors

Dr. David R. Green

E-Mail Website
Guest Editor
UCEMM, Department of Geography, School of Geosciences, University of Aberdeen, Aberdeen AB24 3UF, UK
Interests: UV; GIS; remote sensing; photogrammetry; cartography; digital mapping; coastal management; marine spatial planning; coastal ecology
Special Issues, Collections and Topics in MDPI journals
Dr. Johannes Fahrentrapp

E-Mail Website
Guest Editor
Research Fellow at Research Group for Horticulture of Institute of Natural Resource Sciences, Zurich University of Applied Sciences ZHAW, Grüentlstrasse 14, CH-8820 Wädenswil, Switzerland
Interests: pre-symptomatic disease detection; pest and disease monitoring; pest and disease management, host–pathogen interaction; future agriculture scenarios

Special Issue Information

Dear Colleagues,

The rapid evolution of drones, UAVs, unmanned ground-based vehicles (UGV) and drone-related technologies including software has seen the development of many environmental applications in recent years. Precision agriculture and horticulture have been one such area that has seen a growing role for image acquisition and processing, and more recently 3D models. Drones have been applied to monitor crop area, to estimate yield, crop water and nutritional status, extract forest canopy information, as well as to oversee and monitor animals on farmland. Additionally, several concepts have been developed to both detect and spray diseased crop plants. Several low-cost commercial applications have also been developed to provide the horticulturalist or farmer with ‘plug and play’ solutions that can be easily deployed in the field. For example, they can be used to acquire imagery from different sensors and processed online in the Cloud to provide information for real-time decision-making.

Advances in computer vision and the parallel development of Unmanned Aerial Vehicles (UAVs) allows for extensive use of UAV in forest inventory and indirect measurements of tree features. Artificial intelligence (AI) is also permitting accurate autonomous flight and detailed information extraction for use in farm management decision support systems (DSS). Tree condition, pruning and orchard management practices within intensive horticultural tree crop systems can be determined via measurements of tree structure. In orchards, measuring crown characteristics is essential for monitoring the dynamics of tree growth and optimizing farm management. Progress in the generation of high-resolution 3D canopy models—using Structure from Motion (SfM) software—has led to the extraction of detailed canopy structure information from such imagery. These exploit high-resolution UAV imagery with variables extracted from such imagery contributing to the improved identification of the effects of canopy structure and leaf biochemistry on crops. In other applications, stereo UAV imagery has been used to extract tree canopy heights and multi-spectral imagery has been demonstrated as an accurate and efficient means to measure various tree structural attributes. The determination of tree height is important, mainly because of its biological and commercial importance. It is a significant indicator, which reflects the site productive capacity of the species concerned, when it is growing on a particular site. Image analysis has been used to assess crop development at the emergence stage. It can also facilitate future studies on optimizing fertiliser management and improving emergence consistency Other research will focus on how to use the anisotropy signal as a source of information for the estimation of physical vegetation properties. 

These technological attempts to try to solve numerous problems by analyzing airborne imaging and taking airborne actions (e.g., fungicide applications) require interdisciplinary skills and combine biological and technological knowledge as well as IT skills in terms of programming the drones and analyzing the obtained data. This Special Issue, therefore, welcomes scientific papers from authors working in the field of drone applications in precision agriculture, horticulture, and forestry. It will bring together studies presenting results on crop monitoring, applications working with drones as a practical tool such as the application of fungicides, and drone applications in forestry and livestock, focusing on the extraction of information from UAV imagery for crop management and decision support systems. It will cover technical developments of drones, their sensors, applications, and case studies. The Special Issue will not be limited to aerial drones but will also include ground-based unmanned vehicles as well.

Prof. Dr. David R. Green
Dr. Johannes Fahrentrapp
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. Drones 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

  • UAV
  • crop monitoring
  • 3D modelling
  • artificial intelligence (AI)
  • information extraction
  • management
  • decision-support

Published Papers (4 papers)

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Research

23 pages, 8576 KiB  
Article
Measures of Canopy Structure from Low-Cost UAS for Monitoring Crop Nutrient Status
by Kellyn Montgomery, Josh B. Henry, Matthew C. Vann, Brian E. Whipker, Anders S. Huseth and Helena Mitasova
Drones 2020, 4(3), 36; https://doi.org/10.3390/drones4030036 - 22 Jul 2020
Cited by 8 | Viewed by 4747
Abstract
Deriving crop information from remotely sensed data is an important strategy for precision agriculture. Small unmanned aerial systems (UAS) have emerged in recent years as a versatile remote sensing tool that can provide precisely-timed, fine-grained data for informing management responses to intra-field crop [...] Read more.
Deriving crop information from remotely sensed data is an important strategy for precision agriculture. Small unmanned aerial systems (UAS) have emerged in recent years as a versatile remote sensing tool that can provide precisely-timed, fine-grained data for informing management responses to intra-field crop variability (e.g., nutrient status and pest damage). UAS sensors with high spectral resolution used to compute informative vegetation indices, however, are practically limited by high cost and data dimensionality. This research extends spectral analysis for remote crop monitoring to investigate the relationship between crop health and 3D canopy structure using low-cost UAS equipped with consumer-grade RGB cameras. We used flue-cured tobacco as a case study due to its known sensitivity to fertility variation and nutrient-specific symptomology. Fertilizer treatments were applied to induce plant health variability in a 0.5 ha field of flue-cured tobacco. Multi-view stereo images from three UAS surveys collected during crop development were processed into orthoimages used to compute a visible band spectral index and photogrammetric point clouds using Structure from Motion (SfM). Plant structural metrics were then computed from detailed high resolution canopy surface models (0.05 m resolution) interpolated from the photogrammetric point clouds. The UAS surveys were complimented by nutrient status measurements obtained from plant tissues. The relationships between foliar nitrogen (N), phosphorus (P), potassium (K), and boron (B) concentrations and the UAS-derived metrics were assessed using multiple linear regression. Symptoms of N and K deficiencies were well captured and differentiated by the structural metrics. The strongest relationship observed was between canopy shape and N foliar concentration (adj. r2 = 0.59, increasing to adj. r2 = 0.81 when combined with the spectral index). B foliar concentration was consistently better predicted by canopy structure with a maximum adj. r2 = 0.41 observed at the latest growth stage surveyed. Overall, combining information about canopy structure and spectral reflectance increased model fit for all measured nutrients compared to spectral alone. These results suggest that an important relationship exists between relative canopy shape and crop health that can be leveraged to improve the usefulness of low cost UAS for precision agriculture. Full article
(This article belongs to the Special Issue Precision Agriculture, Horticulture and Forestry: Extracting Canopy Information from Drone Imagery for Management and Decision-Making)
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15 pages, 4397 KiB  
Article
Correlating the Plant Height of Wheat with Above-Ground Biomass and Crop Yield Using Drone Imagery and Crop Surface Model, A Case Study from Nepal
by Uma Shankar Panday, Nawaraj Shrestha, Shashish Maharjan, Arun Kumar Pratihast, Shahnawaz, Kundan Lal Shrestha and Jagannath Aryal
Drones 2020, 4(3), 28; https://doi.org/10.3390/drones4030028 - 01 Jul 2020
Cited by 26 | Viewed by 7579
Abstract
Food security is one of the burning issues in the 21st century, as a tremendous population growth over recent decades has increased demand for food production systems. However, agricultural production is constrained by the limited availability of arable land resources, whereas a significant [...] Read more.
Food security is one of the burning issues in the 21st century, as a tremendous population growth over recent decades has increased demand for food production systems. However, agricultural production is constrained by the limited availability of arable land resources, whereas a significant part of these is already degraded due to overexploitation. In order to get optimum output from the available land resources, it is of prime importance that crops are monitored, analyzed, and mapped at various stages of growth so that the areas having underdeveloped/unhealthy plants can be treated appropriately as and when required. This type of monitoring can be performed using ultra-high-resolution earth observation data like the images captured through unmanned aerial vehicles (UAVs)/drones. The objective of this research is to estimate and analyze the above-ground biomass (AGB) of the wheat crop using a consumer-grade red-green-blue (RGB) camera mounted on a drone. AGB and yield of wheat were estimated from linear regression models involving plant height obtained from crop surface models (CSMs) derived from the images captured by the drone-mounted camera. This study estimated plant height in an integrated setting of UAV-derived images with a Mid-Western Terai topographic setting (67 to 300 m amsl) of Nepal. Plant height estimated from the drone images had an error of 5% to 11.9% with respect to direct field measurement. While R2 of 0.66 was found for AGB, that of 0.73 and 0.70 were found for spike and grain weights respectively. This statistical quality assurance contributes to crop yield estimation, and hence to develop efficient food security strategies using earth observation and geo-information. Full article
(This article belongs to the Special Issue Precision Agriculture, Horticulture and Forestry: Extracting Canopy Information from Drone Imagery for Management and Decision-Making)
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21 pages, 6430 KiB  
Article
Estimating Tree Height and Volume Using Unmanned Aerial Vehicle Photography and SfM Technology, with Verification of Result Accuracy
by Shohei Kameyama and Katsuaki Sugiura
Drones 2020, 4(2), 19; https://doi.org/10.3390/drones4020019 - 11 May 2020
Cited by 34 | Viewed by 7815
Abstract
This study aimed to investigate the effects of differences in shooting and flight conditions for an unmanned aerial vehicle (UAV) on the processing method and estimated results of aerial images. Forest images were acquired under 80 different conditions, combining various aerial photography methods [...] Read more.
This study aimed to investigate the effects of differences in shooting and flight conditions for an unmanned aerial vehicle (UAV) on the processing method and estimated results of aerial images. Forest images were acquired under 80 different conditions, combining various aerial photography methods and flight conditions. We verified errors in values measured by the UAV and the measurement accuracy with respect to tree height and volume. Our results showed that aerial images could be processed under all the studied flight conditions. However, although tree height and crown were decipherable in the created 3D model in 64 conditions, they were undecipherable in 16. The standard deviation (SD) in crown area values for each target tree was 0.08 to 0.68 m2. UAV measurements of tree height tended to be lower than the actual values, and the RMSE (root mean square error) was high (5.2 to 7.1 m) through all the 64 modeled conditions. With the estimated volume being lower than the actual volume, the RMSE volume measurements for each flight condition were from 0.31 to 0.4 m3. Therefore, irrespective of flight conditions for UAV measurements, accuracy was low with respect to the actual values. Full article
(This article belongs to the Special Issue Precision Agriculture, Horticulture and Forestry: Extracting Canopy Information from Drone Imagery for Management and Decision-Making)
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26 pages, 12937 KiB  
Article
Monitoring Selective Logging in a Pine-Dominated Forest in Central Germany with Repeated Drone Flights Utilizing A Low Cost RTK Quadcopter
by Christian Thiel, Marlin M. Müller, Christian Berger, Felix Cremer, Clémence Dubois, Sören Hese, Jussi Baade, Friederike Klan and Carsten Pathe
Drones 2020, 4(2), 11; https://doi.org/10.3390/drones4020011 - 09 Apr 2020
Cited by 13 | Viewed by 4462
Abstract
There is no doubt that unmanned aerial systems (UAS) will play an increasing role in Earth observation in the near future. The field of application is very broad and includes aspects of environmental monitoring, security, humanitarian aid, or engineering. In particular, drones with [...] Read more.
There is no doubt that unmanned aerial systems (UAS) will play an increasing role in Earth observation in the near future. The field of application is very broad and includes aspects of environmental monitoring, security, humanitarian aid, or engineering. In particular, drones with camera systems are already widely used. The capability to compute ultra-high-resolution orthomosaics and three-dimensional (3D) point clouds from UAS imagery generates a wide interest in such systems, not only in the science community, but also in industry and agencies. In particular, forestry sciences benefit from ultra-high-structural and spectral information as regular tree level-based monitoring becomes feasible. There is a great need for this kind of information as, for example, due to the spring and summer droughts in Europe in the years 2018/2019, large quantities of individual trees were damaged or even died. This study focuses on selective logging at the level of individual trees using repeated drone flights. Using the new generation of UAS, which allows for sub-decimeter-level positioning accuracies, a change detection approach based on bi-temporal UAS acquisitions was implemented. In comparison to conventional UAS, the effort of implementing repeated drone flights in the field was low, because no ground control points needed to be surveyed. As shown in this study, the geometrical offset between the two collected datasets was below 10 cm across the site, which enabled a direct comparison of both datasets without the need for post-processing (e.g., image matching). For the detection of logged trees, we utilized the spectral and height differences between both acquisitions. For their delineation, an object-based approach was employed, which was proven to be highly accurate (precision = 97.5%; recall = 91.6%). Due to the ease of use of such new generation, off-the-shelf consumer drones, their decreasing purchase costs, the quality of available workflows for data processing, and the convincing results presented here, UAS-based data can and should complement conventional forest inventory practices. Full article
(This article belongs to the Special Issue Precision Agriculture, Horticulture and Forestry: Extracting Canopy Information from Drone Imagery for Management and Decision-Making)
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