Unmanned Aircraft System (UAS) Technology and Applications in Agriculture

Numerous sensors have been developed over time for precision agriculture; though, only recently have these sensors been incorporated into the new realm of unmanned aircraft systems (UAS). This UAS technology has allowed for a more integrated and optimized approach to various farming tasks such as field mapping, plant stress detection, biomass estimation, weed management, inventory counting, and chemical spraying, among others. These systems can be highly specialized depending on the particular goals of the researcher or farmer, yet many aspects of UAS are similar. All systems require an underlying platform—or unmanned aerial vehicle (UAV)—and one or more peripherals and sensing equipment such as imaging devices (RGB, multispectral, hyperspectral, near infra-red, RGB depth), gripping tools, or spraying equipment. Along with these wide-ranging peripherals and sensing equipment comes a great deal of data processing. Common tools to aid in this processing include vegetation indices, point clouds, machine learning models, and statistical methods. With any emerging technology, there are also a few considerations that need to be analyzed like legal constraints, economic trade-offs, and ease of use. This review then concludes with a discussion on the pros and cons of this technology, along with a brief outlook into future areas of research regarding UAS technology in agriculture.

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UAV-BASED AUTOMATIC TREE GROWTH MEASUREMENT FOR BIOMASS ESTIMATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-685-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 685-688

Author(s):

M. Karpina ◽

M. Jarząbek-Rychard ◽

P. Tymków ◽

A. Borkowski

Keyword(s):

Tree Growth ◽

Point Clouds ◽

In Situ Measurements ◽

Unmanned Aircraft ◽

Volume Estimation ◽

Biomass Estimation ◽

Test Field ◽

3D Point Clouds ◽

Growth Estimation

Manual in-situ measurements of geometric tree parameters for the biomass volume estimation are time-consuming and economically non-effective. Photogrammetric techniques can be deployed in order to automate the measurement procedure. The purpose of the presented work is an automatic tree growth estimation based on Unmanned Aircraft Vehicle (UAV) imagery. The experiment was conducted in an agriculture test field with scots pine canopies. The data was collected using a Leica Aibotix X6V2 platform equipped with a Nikon D800 camera. Reference geometric parameters of selected sample plants were measured manually each week. In situ measurements were correlated with the UAV data acquisition. The correlation aimed at the investigation of optimal conditions for a flight and parameter settings for image acquisition. The collected images are processed in a state of the art tool resulting in a generation of dense 3D point clouds. The algorithm is developed in order to estimate geometric tree parameters from 3D points. Stem positions and tree tops are identified automatically in a cross section, followed by the calculation of tree heights. The automatically derived height values are compared to the reference measurements performed manually. The comparison allows for the evaluation of automatic growth estimation process. The accuracy achieved using UAV photogrammetry for tree heights estimation is about 5cm.

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Remotely Piloted Aircraft (RPA) in Agriculture: A Pursuit of Sustainability

Agronomy ◽

10.3390/agronomy11010007 ◽

2020 ◽

Vol 11 (1) ◽

pp. 7

Author(s):

Ali Ahmad ◽

Javier Ordoñez ◽

Pedro Cartujo ◽

Vanesa Martos

Keyword(s):

Precision Agriculture ◽

Weed Management ◽

Disease Surveillance ◽

Soil Analysis ◽

Vegetation Indices ◽

Tree Plantation ◽

Crop Monitoring ◽

Agriculture Sector ◽

Research Activities ◽

Remotely Piloted Aircraft

The current COVID-19 global pandemic has amplified the pressure on the agriculture sector, inciting the need for sustainable agriculture more than ever. Thus, in this review, a sustainable perspective of the use of remotely piloted aircraft (RPA) or drone technology in the agriculture sector is discussed. Similarly, the types of cameras (multispectral, thermal, and visible), sensors, software, and platforms frequently deployed for ensuring precision agriculture for crop monitoring, disease detection, or even yield estimation are briefly discoursed. In this regard, vegetation indices (VIs) embrace an imperative prominence as they provide vital information for crop monitoring and decision-making, thus a summary of most commonly used VIs is also furnished and serves as a guide while planning to collect specific crop data. Furthermore, the establishment of significant applications of RPAs in livestock, forestry, crop monitoring, disease surveillance, irrigation, soil analysis, fertilization, crop harvest, weed management, mechanical pollination, crop insurance and tree plantation are cited in the light of currently available literature in this domain. RPA technology efficiency, cost and limitations are also considered based on the previous studies that may help to devise policies, technology adoption, investment, and research activities in this sphere.

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UAV-BASED AUTOMATIC TREE GROWTH MEASUREMENT FOR BIOMASS ESTIMATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-685-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 685-688 ◽

Cited By ~ 17

Author(s):

M. Karpina ◽

M. Jarząbek-Rychard ◽

P. Tymków ◽

A. Borkowski

Keyword(s):

Tree Growth ◽

Point Clouds ◽

In Situ Measurements ◽

Unmanned Aircraft ◽

Volume Estimation ◽

Biomass Estimation ◽

Test Field ◽

3D Point Clouds ◽

Growth Estimation

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Terrestrial laser scanning for plant height measurement and biomass estimation of maize

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-181-2014 ◽

2014 ◽

Vol XL-7 ◽

pp. 181-187 ◽

Cited By ~ 9

Author(s):

N. Tilly ◽

D. Hoffmeister ◽

H. Schiedung ◽

C. Hütt ◽

J. Brands ◽

...

Keyword(s):

Plant Height ◽

Precision Agriculture ◽

Laser Scanning ◽

Point Clouds ◽

Time Frame ◽

Coefficient Of Determination ◽

Biomass Estimation ◽

Height Measurement ◽

Growing Period ◽

Non Destructive

Over the last decades, the role of remote sensing gained in importance for monitoring applications in precision agriculture. A key factor for assessing the development of crops during the growing period is the actual biomass. As non-destructive methods of directly measuring biomass do not exist, parameters like plant height are considered as estimators. In this contribution, first results of multitemporal surveys on a maize field with a terrestrial laser scanner are shown. The achieved point clouds are interpolated to generate Crop Surface Models (CSM) that represent the top canopy. These CSMs are used for visualizing the spatial distribution of plant height differences within the field and calculating plant height above ground with a high resolution of 1 cm. In addition, manual measurements of plant height were carried out corresponding to each TLS campaign to verify the results. The high coefficient of determination (R² = 0.93) between both measurement methods shows the applicability of the presented approach. The established regression model between CSM-derived plant height and destructively measured biomass shows a varying performance depending on the considered time frame during the growing period. This study shows that TLS is a suitable and promising method for measuring plant height of maize. Moreover, it shows the potential of plant height as a non-destructive estimator for biomass in the early growing period. However, challenges are the non-linear development of plant height and biomass over the whole growing period.

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Vegetable Crop Biomass Estimation Using Hyperspectral and RGB 3D UAV Data

Agronomy ◽

10.3390/agronomy10101600 ◽

2020 ◽

Vol 10 (10) ◽

pp. 1600

Author(s):

Thomas Astor ◽

Supriya Dayananda ◽

Sunil Nautiyal ◽

Michael Wachendorf

Keyword(s):

Precision Agriculture ◽

Prediction Accuracy ◽

Production Systems ◽

Predictive Performance ◽

Point Clouds ◽

Biomass Estimation ◽

Vegetable Crops ◽

Fresh Matter ◽

Sensory Data ◽

Increasing Trends

Remote sensing (RS) has been an effective tool to monitor agricultural production systems, but for vegetable crops, precision agriculture has received less interest to date. The objective of this study was to test the predictive performance of two types of RS data—crop height information derived from point clouds based on RGB UAV data, and reflectance information from terrestrial hyperspectral imagery—to predict fresh matter yield (FMY) for three vegetable crops (eggplant, tomato, and cabbage). The study was conducted in an experimental layout in Bengaluru, India, at five dates in summer 2017. The prediction accuracy varied strongly depending on the RS dataset used. For all crops, a good predictive performance with cross-validated prediction error < 10% was achieved. The growth stage of the crops had no significant effect on the prediction accuracy, although increasing trends of an underestimation of FMY with later sampling dates for eggplant and tomato were found. The study proves that an estimation of vegetable FMY using RS data is successful throughout the growing season. Different RS datasets were best for biomass prediction of the three vegetables, indicating that multi-sensory data collection should be preferred to single sensor use, as no one sensor system is superior.

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Prediction of Biomass and N Fixation of Legume–Grass Mixtures Using Sensor Fusion

Frontiers in Plant Science ◽

10.3389/fpls.2020.603921 ◽

2021 ◽

Vol 11 ◽

Author(s):

Esther Grüner ◽

Thomas Astor ◽

Michael Wachendorf

Keyword(s):

Sensor Fusion ◽

Precision Agriculture ◽

Laser Scanning ◽

Near Infrared ◽

Vegetation Indices ◽

Point Clouds ◽

N Fixation ◽

Estimation Model ◽

Cloud Data ◽

Plant Trait

European farmers and especially organic farmers rely on legume–grass mixtures in their crop rotation as an organic nitrogen (N) source, as legumes can fix atmospheric N, which is the most important element for plant growth. Furthermore, legume–grass serves as valuable fodder for livestock and biogas plants. Therefore, information about aboveground biomass and N fixation (NFix) is crucial for efficient farm management decisions on the field level. Remote sensing, as a non-destructive and fast technique, provides different methods to quantify plant trait parameters. In our study, high-density point clouds, derived from terrestrial laser scanning (TLS), in combination with unmanned aerial vehicle-based multispectral (MS) data, were collected to receive information about three plant trait parameters (fresh and dry matter, nitrogen fixation) in two legume–grass mixtures. Several crop surface height metrics based on TLS and vegetation indices based on the four MS bands (green, red, red edge, and near-infrared) were calculated. Furthermore, eight texture features based on mean crop surface height and the four MS bands were generated to measure horizontal spatial heterogeneity. The aim of this multi-temporal study over two vegetation periods was to create estimation models based on biomass and N fixation for two legume–grass mixtures by sensor fusion, a combination of both sensors. To represent conditions in practical farming, e.g., the varying proportion of legumes, the experiment included pure stands of legume and grass of the mixtures. Sensor fusion of TLS and MS data was found to provide better estimates of biomass and NFix than separate data analysis. The study shows the important role of texture based on MS and point cloud data, which contributed greatly to the estimation model generation. The applied approach offers an interesting method for improvements in precision agriculture.

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Comparing methods to estimate perennial ryegrass biomass: canopy height and spectral vegetation indices

Precision Agriculture ◽

10.1007/s11119-020-09737-z ◽

2020 ◽

Vol 22 (1) ◽

pp. 205-225 ◽

Cited By ~ 3

Author(s):

Gustavo Togeiro de Alckmin ◽

Lammert Kooistra ◽

Richard Rawnsley ◽

Arko Lucieer

Keyword(s):

Perennial Ryegrass ◽

Precision Agriculture ◽

Vegetation Indices ◽

Multivariate Adaptive Regression Splines ◽

Support Vector ◽

Biomass Estimation ◽

Pasture Management ◽

Accuracy And Precision ◽

Spectral Vegetation Indices ◽

Regression Techniques

AbstractPasture management is highly dependent on accurate biomass estimation. Usually, such activity is neglected as current methods are time-consuming and frequently perceived as inaccurate. Conversely, spectral data is a promising technique to automate and improve the accuracy and precision of estimates. Historically, spectral vegetation indices have been widely adopted and large numbers have been proposed. The selection of the optimal index or satisfactory subset of indices to accurately estimate biomass is not trivial and can influence the design of new sensors. This study aimed to compare a canopy-based technique (rising plate meter) with spectral vegetation indices. It examined 97 vegetation indices and 11,026 combinations of normalized ratio indices paired with different regression techniques on 900 pasture biomass data points of perennial ryegrass (Lolium perenne) collected throughout a 1-year period. The analyses demonstrated that the canopy-based technique is superior to the standard normalized difference vegetation index (∆, 115.1 kg DM ha−1 RMSE), equivalent to the best performing normalized ratio index and less accurate than four selected vegetation indices deployed with different regression techniques (maximum ∆, 231.1 kg DM ha−1). When employing the four selected vegetation indices, random forests was the best performing regression technique, followed by support vector machines, multivariate adaptive regression splines and linear regression. Estimate precision was improved through model stacking. In summary, this study demonstrated a series of achievable improvements in both accuracy and precision of pasture biomass estimation, while comparing different numbers of inputs and regression techniques and providing a benchmark against standard techniques of precision agriculture and pasture management.

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Legal constraints on technologies

Manuali – Scienze Tecnologiche - SPARKLE - Entrepreneurship for Sustainable Precision Agriculture ◽

10.36253/978-88-5518-044-3.36 ◽

2020 ◽

pp. 36

Author(s):

Stefanos Nastis

Keyword(s):

Artificial Intelligence ◽

Precision Agriculture ◽

Current Situation ◽

Unmanned Aircraft ◽

Space Agency ◽

Legal Constraints ◽

Ethics Guidelines ◽

The Eu ◽

New Jobs

The legal constraints of two important technologies for sustainable precision agriculture are presented: unmanned aircraft and artificial intelligence. Unmanned aircraft, or drones, are a rapidly developing technology. By 2035, it is estimated that in the EU, drones will create over 100,000 new jobs and produce more than 10 billion euros per year in revenue. The current situation regarding drone operation is detailed, along with the recommendations of the European Aviation and Space Agency (EASA). Furthermore, the procedure for obtaining a commercial drone permit is briefly described and the situations where such a permit may be required are presented. Finally, the course concludes with the latest EU regulations on ethical use of Artificial Intelligence, presenting the ethics guidelines of the EU for trustworthy AI.

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QVigourMap: A GIS Open Source Application for the Creation of Canopy Vigour Maps

Agronomy ◽

10.3390/agronomy11050952 ◽

2021 ◽

Vol 11 (5) ◽

pp. 952

Author(s):

Lia Duarte ◽

Ana Cláudia Teodoro ◽

Joaquim J. Sousa ◽

Luís Pádua

Keyword(s):

Open Source ◽

Precision Agriculture ◽

Vegetation Indices ◽

Data Interpretation ◽

Vegetation Monitoring ◽

Distribution Maps ◽

Monitoring Process ◽

Multi Temporal ◽

The Creation ◽

The Right

In a precision agriculture context, the amount of geospatial data available can be difficult to interpret in order to understand the crop variability within a given terrain parcel, raising the need for specific tools for data processing and analysis. This is the case for data acquired from Unmanned Aerial Vehicles (UAV), in which the high spatial resolution along with data from several spectral wavelengths makes data interpretation a complex process regarding vegetation monitoring. Vegetation Indices (VIs) are usually computed, helping in the vegetation monitoring process. However, a crop plot is generally composed of several non-crop elements, which can bias the data analysis and interpretation. By discarding non-crop data, it is possible to compute the vigour distribution for a specific crop within the area under analysis. This article presents QVigourMaps, a new open source application developed to generate useful outputs for precision agriculture purposes. The application was developed in the form of a QGIS plugin, allowing the creation of vigour maps, vegetation distribution maps and prescription maps based on the combination of different VIs and height information. Multi-temporal data from a vineyard plot and a maize field were used as case studies in order to demonstrate the potential and effectiveness of the QVigourMaps tool. The presented application can contribute to making the right management decisions by providing indicators of crop variability, and the outcomes can be used in the field to apply site-specific treatments according to the levels of vigour.

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Seismic Damage Semantics on Post-Earthquake LOD3 Building Models Generated by UAS

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10050345 ◽

2021 ◽

Vol 10 (5) ◽

pp. 345

Author(s):

Konstantinos Chaidas ◽

George Tataris ◽

Nikolaos Soulakellis

Keyword(s):

Point Clouds ◽

Recovery Phase ◽

Seismic Damage ◽

Unmanned Aircraft ◽

Earthquake Scenario ◽

Geography Markup Language ◽

3D Point Clouds ◽

Building Models ◽

Building Walls ◽

Oblique Images

In a post-earthquake scenario, the semantic enrichment of 3D building models with seismic damage is crucial from the perspective of disaster management. This paper aims to present the methodology and the results for the Level of Detail 3 (LOD3) building modelling (after an earthquake) with the enrichment of the semantics of the seismic damage based on the European Macroseismic Scale (EMS-98). The study area is the Vrisa traditional settlement on the island of Lesvos, Greece, which was affected by a devastating earthquake of Mw = 6.3 on 12 June 2017. The applied methodology consists of the following steps: (a) unmanned aircraft systems (UAS) nadir and oblique images are acquired and photogrammetrically processed for 3D point cloud generation, (b) 3D building models are created based on 3D point clouds and (c) 3D building models are transformed into a LOD3 City Geography Markup Language (CityGML) standard with enriched semantics of the related seismic damage of every part of the building (walls, roof, etc.). The results show that in following this methodology, CityGML LOD3 models can be generated and enriched with buildings’ seismic damage. These models can assist in the decision-making process during the recovery phase of a settlement as well as be the basis for its monitoring over time. Finally, these models can contribute to the estimation of the reconstruction cost of the buildings.

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