Adoption of an Unmanned Helicopter for Low-Altitude Remote Sensing to Estimate Yield and Total Biomass of a Rice Crop

Swain, Kishore Chandra; Thomson, Steven J.; Jayasuriya, H. P. W.

doi:10.13031/2013.29493

Cited by 149 publications

(45 citation statements)

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“…The helicopter payload is larger than that of a multi-rotor UAV and can support large sensors, such as LIDAR. However, the complex operation, lack of free hover, high maintenance cost and noise limit the application of helicopters (Sugiura et al, 2005; Swain et al, 2010; Chapman et al, 2014). The fixed-wing UAV is characterized by fast flying velocity and long flight time; however, the bottleneck for the fixed-wing application of FBP is the lack of free hover ability and the image blur caused by higher speeds and altitudes (Herwitz et al, 2004; Link et al, 2013).…”

Section: Uav Platforms and Deployed Sensorsmentioning

confidence: 99%

“…Linear regression methods, including multivariate linear regression (MLR), stepwise linear regression (SLR), and PLSR, are simple and feasible (Capolupo et al, 2015) and have wide applications in quantitative analysis by remote sensing. For example, MLR was used to estimate crop biomass (Rosen et al, 2006; Swain et al, 2010), PLSR was used to estimate the LAI in soybean breeding (Lu et al, 2016), and SLR was used to estimate the leaf chlorophyll content (Berni et al, 2009b). However, the primary objection to linear regression methods is the lack of explanation of crop physiology.…”

Section: Advances Of Uav Remote Sensing In Field Phenotypingmentioning

confidence: 99%

“…The sensors including multispectral camera, hyperspectral imager, LIDAR, combing the methods including empirical statistical analysis of vegetation indices, estimation based on net primary productivity, and crop growth model have been widely deployed for biomass estimation (Hunt et al, 2005; Swain et al, 2010; Wallace et al, 2012; Bendig et al, 2014). However, the method for the estimation of crop biomass by UAV remote sensing is mainly empirical statistical model.…”

Section: Advances Of Uav Remote Sensing In Field Phenotypingmentioning

confidence: 99%

“…Digital cameras, multispectral cameras, hyperspectral sensors, infrared thermal imagers, and light detection and ranging (LIDAR) are commonly deployed UAV-RSP sensors. The applications of these sensors for FBP include visible imaging for canopy surface modeling, crop height and biomass estimation (Mathews and Jensen, 2013; Diaz-Varela et al, 2014; Zarco-Tejada et al, 2014), visible–near-infrared spectroscopy to identify physiological status (Sugiura et al, 2005; Overgaard et al, 2010; Swain et al, 2010; Nigon et al, 2015), thermal imaging to detect water stress (Gonzalez-Dugo et al, 2013, 2014), LIDAR point cloud to measure plant fine-scale geometric parameters with high precision (Wallace et al, 2012), and microwave images for estimating soil moisture and canopy structure parameters by combining different spectral bands (Acevo-Herrera et al, 2010; Issei et al, 2010). …”

Section: Introductionmentioning

confidence: 99%

“…(Hunt et al, 2005, 2010; Li J. W. et al, 2015). The absorption and reflectance characteristics of crops can be used to retrieve the physiological characteristics of a crop (Overgaard et al, 2010; Swain et al, 2010; Nigon et al, 2015). The canopy temperature is closely related to crop transpiration, which can reflect the leaf water potential, stomatal conductance, etc.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives

et al. 2017

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Phenotyping plays an important role in crop science research; the accurate and rapid acquisition of phenotypic information of plants or cells in different environments is helpful for exploring the inheritance and expression patterns of the genome to determine the association of genomic and phenotypic information to increase the crop yield. Traditional methods for acquiring crop traits, such as plant height, leaf color, leaf area index (LAI), chlorophyll content, biomass and yield, rely on manual sampling, which is time-consuming and laborious. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become an important approach for fast and non-destructive high throughput phenotyping and have the advantage of flexible and convenient operation, on-demand access to data and high spatial resolution. UAV-RSPs are a powerful tool for studying phenomics and genomics. As the methods and applications for field phenotyping using UAVs to users who willing to derive phenotypic parameters from large fields and tests with the minimum effort on field work and getting highly reliable results are necessary, the current status and perspectives on the topic of UAV-RSPs for field-based phenotyping were reviewed based on the literature survey of crop phenotyping using UAV-RSPs in the Web of Science™ Core Collection database and cases study by NERCITA. The reference for the selection of UAV platforms and remote sensing sensors, the commonly adopted methods and typical applications for analyzing phenotypic traits by UAV-RSPs, and the challenge for crop phenotyping by UAV-RSPs were considered. The review can provide theoretical and technical support to promote the applications of UAV-RSPs for crop phenotyping.

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Section: Uav Platforms and Deployed Sensorsmentioning

confidence: 99%

Section: Advances Of Uav Remote Sensing In Field Phenotypingmentioning

confidence: 99%

Section: Advances Of Uav Remote Sensing In Field Phenotypingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives

et al. 2017

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Nano and Micro Unmanned Aerial Vehicles (UAVs): A New Grand Challenge for Precision Agriculture?

et al. 2020

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By collecting data at spatial and temporal scales that are inaccessible to satellite and field observation, unmanned aerial vehicles (UAVs) are revolutionizing a number of scientific and management disciplines. UAVs may be particularly valuable for precision agricultural applications, offering strong potential to improve the efficiency of water, nutrient, and disease management. However, some authors have suggested that the UAV industry has overhyped the potential value of this technology for agriculture, given that it is difficult for non‐specialists to operate UAVs as well as to process and interpret the resulting data. Here, we analyze the barriers to applying UAVs for precision agriculture, which range from regulatory issues to technical requirements. We then evaluate how new developments in the nano‐ and micro‐UAV (NAV and MAV, respectively) markets may help to overcome these barriers. Among the possible breakthroughs that we identify is the ability of NAV/MAV platforms to directly quantify plant traits using methods (e.g., object‐oriented classification) that require less image calibration and interpretation than spectral index–based approaches. We suggest that this potential, when combined with steady improvements in sensor miniaturization, flight precision, and autonomy as well as cloud‐based image processing, will make UAVs a tool with much broader adoption by agricultural managers in the near future. If this wider uptake is realized, then UAVs have real potential to improve agriculture's resource‐use efficiency. © 2020 by John Wiley & Sons, Inc.

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Drone‐based imaging sensors, techniques, and applications in plant phenotyping for crop breeding: A comprehensive review

Gano,

Bhadra,

Vilbig

et al. 2024

The Plant Phenome Journal

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Over the last decade, the use of unmanned aerial vehicles (UAVs) for plant phenotyping and field crop monitoring has significantly evolved and expanded. These technologies have been particularly valuable for monitoring crop growth and health and for managing abiotic and biotic stresses such as drought, fertilization deficiencies, disease, and bioaggressors. This paper provides a comprehensive review of the progress in UAV‐based plant phenotyping, with a focus on the current use and application of drone technology to gain information on plant growth, development, adaptation, and yield. We reviewed over 200 research articles and discuss the best tools and methodologies for different research purposes, the challenges that need to be overcome, and the major research gaps that remain. First, the review offers a critical focus on elucidating the distinct characteristics of UAV platforms, highlighting the diverse sensor technologies employed and shedding light on the nuances of UAV data acquisition and processing methodologies. Second, it presents a comprehensive analysis of the multiple applications of UAVs in field phenotyping, underscoring the transformative potential of integrating machine learning techniques for plant analysis. Third, it delves into the realm of machine learning applications for plant phenotyping, emphasizing its role in enhancing data analysis and interpretation. Furthermore, the paper extensively examines the open issues and research challenges within the domain, addressing the complexities and limitations faced during data acquisition, processing, and interpretation. Finally, it outlines the future trends and emerging technologies in the field of UAV‐based plant phenotyping, paving the way for innovative advancements and methodologies.

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Adoption of an Unmanned Helicopter for Low-Altitude Remote Sensing to Estimate Yield and Total Biomass of a Rice Crop

Cited by 149 publications

References 40 publications

Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives

Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives

Nano and Micro Unmanned Aerial Vehicles (UAVs): A New Grand Challenge for Precision Agriculture?

Drone‐based imaging sensors, techniques, and applications in plant phenotyping for crop breeding: A comprehensive review

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