UAV applications in wheat crop: a bibliometric approach to the literature

Henrique dos Santos Felipetto, Erivelto Mercante, Octavio Henrique Vianna, Daniel Carvalho Granemann, Adão Robson Elias


The main objective of this study was to carry out a bibliometric search in the literature on the use of UAVs in the wheat crop. For this purpose, a search of all scientific articles published until 2021 was carried out in the Web of Science database. Subsequently, bibliometric literature analysis techniques were applied using the software VOSviewer, which allowed evaluating the co-authorships between countries and institutions and the co-occurrences of words between studies. The journals and authors that publish the most on this topic were verified. The results indicate a growing trend of publications on UAV applications in the last 7 years, with China, the United States, and the United Kingdom being the main researchers on this topic. However, China stands out with approximately 40% of the publications. This analysis reveals the main current issues and the most influential institutions around the world that have carried out relevant research in scientific publications, showing the journals that include more publications and the collaborative patterns related to the use of UAVs in the wheat crop. Multi-rotor platforms with embedded multispectral cameras are the most used for this purpose. About 27.8% of the publications are from the topic related to the monitoring of productivity/phenotyping. Therefore, this application is in evidence, but further studies on the use of drones in regions with high wheat production, such as South American countries, are needed.


Trends. Wheat Production. VOSviewer.

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BANERJEE, B. P.; SPANGENBERG, G.; KANT, S. Fusion of spectral and structural information from aerial images for improved biomass estimation. Remote Sensing, v. 12, n. 19, out. 2020.

BHANDARI, M. et al. Assessing winter wheat foliage disease severity using aerial imagery acquired from small Unmanned Aerial Vehicle (UAV). Computers and Electronics in Agriculture, v. 176, set. 2020.

BIRCH, T.; REYES, E. Forty years of coastal zone management (1975-2014): evolving theory, policy and practice as reflected in scientific research publications. Ocean and Coastal Management, v. 153, nov. 2017, p. 1–11, 2018.

BOHNENKAMP, D.; BEHMANN, J.; MAHLEIN, A.-K. In-field detection of yellow rust in wheat on the ground canopy and UAV scale. Remote Sensing, v. 11, n. 21, nov. 2019.

BRIZOLA; FANTIN Revisão da literatura e revisão sistemática da literatura. Relva - Revista de Educação do Vale do Arianos, v. 3, n. 2, pag 23-39, 2016.

BUKOWIECKI, J. et al. High-throughput prediction of whole season green area index in winter wheat with an airborne multispectral sensor. Frontiers in Plant Science, v. 10, fev. 2020.

BURKART, A. et al. Angular dependency of hyperspectral measurements over wheat characterized by a novel UAV based goniometer. Remote Sensing, v. 7, n. 1, p. 725–746, jan. 2015.

CHEN, Z. et al. In-season diagnosis of winter wheat nitrogen status in smallholder farmer fields across a village using unmanned aerial vehicle-based remote sensing. Agronomy-Basel, v. 9, n. 10, out. 2019.

DONG, T. et al. Assessment of red-edge vegetation indices for crop leaf area index estimation. Remote Sensing of Environment, v. 222, n. December 2018, p. 133–143, 2019.

DU, M.; NOGUCHI, N. Monitoring of wheat growth status and mapping of wheat yield’s within-field spatial variations using color images acquired from UAV-camera system. Remote Sensing, v. 9, n. 3, p. 289, 21 mar. 2017.

DUAN, P.; WANG, Y.; YIN, P. Remote sensing applications in monitoring of protected areas : a bibliometric analysis. Remote Sens., v. 12, n. 5, 772; 2020.

FERNANDEZ-GALLEGO, J. A. et al. Automatic wheat ear counting using machine learning based on RGB UAV imagery. Plant Journal, v. 103, n. 4, p. 1603–1613, ago. 2020.

FERREIRA, A. G. C. Bibliometria na avaliação de periódicos científicos. Revista de Ciência da Informação, v. 11, n. 3, p. 1–9, 2010.

FIORENTINI, M.; ZENOBI, S.; ORSINI, R. Remote and proximal sensing applications for durum wheat nutritional status detection in mediterranean area. Agriculture-Basel, v. 11, n. 1, jan. 2021.

FU, Y. et al. Winter wheat nitrogen status estimation using UAV-based RGB imagery and Gaussian processes regression. Remote Sensing, v. 12, n. 22, nov. 2020a.

FU, Y. et al. Improved estimation of winter wheat aboveground biomass using multiscale textures extracted from UAV-based digital images and hyperspectral feature analysis. Remote Sensing, v. 13, n. 4, fev. 2021.

FU, Z. et al. Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. Remote Sensing, v. 11, n. 3, p. 858–881, set. 2019.

FU, Z. et al. Wheat growth monitoring and yield estimation based on multi-rotor unmanned aerial vehicle. Remote Sensing, v. 12, n. 3, 2020b.

FU, Z. et al. Wheat Growth Monitoring and Yield Estimation based on Multi-Rotor Unmanned Aerial Vehicle. Remote Sensing, v. 12, n. 3, fev. 2020c.

GUAN, S. et al. Assessing correlation of high-resolution ndvi with fertilizer application level and yield of rice and wheat crops using small UAVs. Remote Sensing, v. 11, n. 2, jan. 2019.

GUO, A. et al. Wheat yellow rust detection using UAV-based hyperspectral technology. Remote Sensing, v. 13, n. 1, jan. 2021.

HAGHIGHATTALAB, A. et al. Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. Plant Methods, v. 12, jun. 2016.

HASAN, U.; SAWUT, M.; CHEN, S. Estimating the leaf area index of winter wheat based on unmanned aerial vehicle RGB-image parameters. Sustainability, v. 11, n. 23, dez. 2019.

HASSAN, M. A. et al. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Science, v. 282, p. 95–103, maio 2019.

HEIDARIAN DEHKORDI, R. et al. Monitoring wheat leaf rust and stripe rust in winter wheat using high-resolution UAV-based red-green-blue imagery. Remote Sensing, v. 12, n. 22, nov. 2020.

HOLMAN, F. H. et al. Radiometric calibration of ``commercial off the shelf’ cameras for UAV based high-resolution temporal crop phenotyping of reflectance and NDVI. Remote Sensing, v. 11, n. 14, jul. 2019.

HONKAVAARA, E. et al. Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture. Remote Sensing, v. 5, n. 10, p. 5006–5039, out. 2013.

HONKAVAARA, E.; KHORAMSHAHI, E. Radiometric correction of close-range spectral image blocks captured using an unmanned aerial vehicle with a radiometric block adjustment. Remote Sensing, v. 10, n. 2, fev. 2018.

HU, P.; CHAPMAN, S. C.; ZHENG, B. Coupling of machine learning methods to improve estimation of ground coverage from unmanned aerial vehicle (UAV) imagery for high-throughput phenotyping of crops. Functional Plant Biology, v. 48, n. 8, p. 766-779, 2021.

JIANG, J. et al. Analysis and evaluation of the image preprocessing process of a six-band multispectral camera mounted on an unmanned aerial vehicle for winter wheat monitoring. Sensors, v. 19, n. 3, fev. 2019a.

JIANG, J. et al. Using digital cameras on an unmanned aerial vehicle to derive optimum color vegetation indices for leaf nitrogen concentration monitoring in winter wheat. Remote Sensing, v. 11, n. 22, nov. 2019b.

JIANG, J. et al. Use of an active canopy sensor mounted on an unmanned aerial vehicle to monitor the growth and nitrogen status of winter wheat. Remote Sensing, v. 12, n. 22, nov. 2020.

JIN, X. et al. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment, v. 198, p. 105–114, set. 2017.

KANNING, M. et al. High-resolution UAV-based hyperspectral imagery for LAI and chlorophyll estimations from wheat for yield prediction. Remote Sensing, v. 10, n. 12, dez. 2018.

KHADKA, K. et al. Does leaf waxiness confound the use of NDVI in the assessment of chlorophyll when evaluating genetic diversity panels of wheat? Agronomy-Basel, v. 11, n. 3, mar. 2021.

LATIF, M. A. et al. Mapping wheat response to variations in N, P, Zn, and irrigation using an unmanned aerial vehicle. International Journal of Remote Sensing, v. 39, n. 21, p. 7172 7188, 2018.

LELONG, C. C. D. et al. Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors, v. 8, n. 5, p. 3557–3585, maio 2008.

LI, J. et al. Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery. Plant Methods, v. 15, n. 1, nov. 2019.

LIU, F. et al. Global research trends of geographical information system from 1961 to 2010: a bibliometric analysis. Scientometrics, v. 106, n. 2, p. 751–768, 2016.

LIU, H. et al. Quantitative analysis and hyperspectral remote sensing of the nitrogen nutrition index in winter wheat. International Journal of Remote Sensing, v. 41, n. 3, p. 858–881, fev. 2020a.

LIU, L. et al. Monitoring wheat fusarium head blight using unmanned aerial vehicle hyperspectral imagery. Remote Sensing, v. 12, n. 22, nov. 2020b.

LIU, T. et al. Evaluation of seed emergence uniformity of mechanically sown wheat with UAV RGB imagery. Remote Sensing, v. 9, n. 12, dez. 2017.

LU, N. et al. Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial vehicle system. Plant Methods, v. 15, fev. 2019a.

LU, N. et al. Estimation of nitrogen nutrition status in winter wheat from unmanned aerial vehicle based multi-angular multispectral imagery. frontiers in Plant Science, v. 10, dez. 2019b.

MA, J. et al. Segmenting ears of winter wheat at flowering stage using digital images and deep learning. Computers and Electronics in Agriculture, v. 168, jan. 2020.

MADEC, S. et al. Ear density estimation from high resolution RGB imagery using deep learning technique. Agricultural and Forest Meteorology, v. 264, p. 225–234, jan. 2019.

MAO, W.; WANG, Y.; WANG, Y. Real-time detection of between-row weeds using machine vision. Proceedings of the 2003 ASAE annual meeting. American Society of Agricultural and Biological Engineers, Las Vegas, NV, USA, 27–30, july 2003.

MATEEN, A.; ZHU, Q. Weed detection in wheat crop using UAV for precision agriculture. Pakistan Journal of Agricultural Sciences, v. 56, n. 3, p. 809–817, set. 2019.

MENGMENG, D. et al. Multi-temporal monitoring of wheat growth by using images from satellite and unmanned aerial vehicle. International Journal of Agricultural and Biological Engineering, v. 10, n. 5, p. 1–13, set. 2017.

MOGHIMI, A.; YANG, C.; ANDERSON, J. A. Aerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat. Computers and Electronics in Agriculture, v. 172, maio 2020.

MOZGERIS, G. et al. Imaging from manned ultra-light and unmanned aerial vehicles for estimating properties of spring wheat. Precision Agriculture, v. 19, n. 5, p. 876–894, out. 2018.

OLCZYK, M. Bibliometric approach to tracking the concept of international competitiveness. Journal of Business Economics and Management, v. 17, n. 6, p. 945–959, 2016.

OSTOS-GARRIDO, F. J. et al. High-throughput phenotyping of bioethanol potential in cereals using UAV-based multi-spectral imagery. Frontiers in Plant Science, v. 10, jul. 2019.

OVERGAARD, S. I. et al. Comparisons of two hand-held, multispectral field radiometers and a hyperspectral airborne imager in terms of predicting spring wheat grain yield and quality by means of powered partial least squares regression. Journal of Near Infrared Spectroscopy, v. 18, n. 4, p. 247–261, 2010.

RASMUSSEN, J. et al. Pre-harvest weed mapping of cirsium arvense in wheat and barley with off-the-shelf UAVs. Precision Agriculture, v. 20, n. 5, p. 983–999, out. 2019.

REVILL, A. et al. The value of Sentinel-2 spectral bands for the assessment of winter wheat growth and development. Remote Sensing, v. 11, n. 17, set. 2019.

REVILL, A. et al. Quantifying uncertainty and bridging the scaling gap in the retrieval of leaf area index by coupling Sentinel-2 and UAV observations. Remote Sensing, v. 12, n. 11, jun. 2020.

ROOSJEN, P. P. J. et al. Hyperspectral reflectance anisotropy measurements using a pushbroom spectrometer on an unmanned aerial vehicle-results for barley, winter wheat, and potato. Remote Sensing, v. 8, n. 11, nov. 2016.

SADEGHI-TEHRAN, P. et al. Deepcount: in-field automatic quantification of wheat spikes using simple linear iterative clustering and deep convolutional neural networks. Frontiers in Plant Science, v. 10, set. 2019.

SANKARAN, S.; KHOT, L. R.; CARTER, A. H. Field-based crop phenotyping: multispectral aerial imaging for evaluation of winter wheat emergence and spring stand. Computers and Electronics in Agriculture, v. 118, p. 372–379, 1 out. 2015.

SCHIRRMANN, M. et al. Monitoring agronomic parameters of winter wheat crops with low-cost UAV imagery. Remote Sensing, v. 8, n. 9, set. 2016.

SENTHILNATH, J. et al. Detection of tomatoes using spectral-spatial methods in remotely sensed RGB images captured by UAV. Biosystems Engineering, v. 146, p. 16–32, 1 jun. 2016.

SHAFIEE, S. et al. Sequential forward selection and support vector regression in comparison to LASSO regression for spring wheat yield prediction based on UAV imagery. Computers and Electronics in Agriculture, v. 183, abr. 2021.

SOUZA, R. G. Mercado Internacional. Conab, n. 61, p. 1–5, 2018.

SU, J. et al. Spatio-temporal monitoring of wheat yellow rust using UAV multispectral imagery. Computers and Electronics in Agriculture, v. 167, dez. 2019.

SU, J. et al. Aerial visual perception in smart farming: field study of wheat yellow rust monitoring. IEEE Transactions on Industrial Informatics, v. 17, n. 3, p. 2242–2249, mar. 2021.

TAO, H. et al. Estimation of crop growth parameters using UAV-based hyperspectral remote sensing data. Sensors, v. 20, n. 5, mar. 2020.

TORRES-SANCHEZ, J.; LOPEZ-GRANADOS, F.; PENA, J. M. An automatic object-based method for optimal thresholding in UAV images: application for vegetation detection in herbaceous crops. Computers and Electronics in Agriculture, v. 114, p. 43–52, jun. 2015.

VAN ECK, N. J.; WALTMAN, L. VOSviewer manual. Leiden, Univeristeit Leiden, nov., 2013.

VEGA, F. A. et al. Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop. Biosystems Engineering, v. 132, p. 19–27, 1 abr. 2015.

VOLPATO, L. et al. High throughput field phenotyping for plant height using UAV-based RGB imagery in wheat breeding lines: feasibility and validation. Frontiers in Plant Science, v. 12, fev. 2021.

WAN, L. et al. Combining UAV-based vegetation indices and image classification to estimate flower number in oilseed rape. Remote Sensing, v. 10, n. 9, p. 1484, 17 set. 2018.

WANG, L. et al. Bibliometric analysis of remote sensing research trend in crop growth monitoring: a case study in china. Remote Sens, v. 11, n. 7, 809; 2019.

WU, Q. et al. Field monitoring of wheat seedling stage with hyperspectral imaging. International Journal of Agricultural and Biological Engineering, v. 9, n. 5, p. 143–148, 2016.

YAO, L. et al. UAV-borne dual-band sensor method for monitoring physiological crop status. Sensors, v. 19, n. 4, fev. 2019.

YAO, X. et al. Estimation of wheat LAI at middle to high levels using unmanned aerial vehicle narrowband multispectral imagery, Remote Sensing, v. 9, n. 12, 2017.

YUE, J. et al. A comparison of crop parameters estimation using images from UAV-mounted snapshot hyperspectral sensor and high-definition digital camera. Remote Sensing, v. 10, n. 7, 1 jul. 2018.

YUE, J. et al. Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices. ISPRS Journal of Photogrammetry and Remote Sensing, v. 150, p. 226–244, abr. 2019.

ZHANG, S. et al. Integrated satellite, unmanned aerial vehicle (UAV) and ground inversion of the SPAD of winter wheat in the reviving stage. Sensors, v. 19, n. 7, abr. 2019a.

ZHANG, T. et al. State and parameter estimation of the AquaCrop model for winter wheat using sensitivity informed particle filter. Computers and Electronics in Agriculture, v. 180, jan. 2021.

ZHANG, X. et al. A deep learning-based approach for automated yellow rust disease detection from high-resolution hyperspectral UAV images. Remote Sensing, v. 11, n. 13, jul. 2019b.

ZHAO, H. et al. Monitoring of nitrogen and grain protein content in winter wheat based on Sentinel-2A data. Remote Sensing, v. 11, n. 14, jul. 2019.

ZHAO, J. et al. Fusion of unmanned aerial vehicle panchromatic and hyperspectral images combining joint skewness-kurtosis figures and a non-subsampled contourlet transform. Sensors, v. 18, n. 10, out. 2018.

ZHENG, H. et al. A comparative assessment of different modeling algorithms for estimating leaf nitrogen content in winter wheat using multispectral images from an unmanned aerial vehicle. Remote Sensing, v. 10, n. 12, dez. 2018.

ZHENG, Q. et al. Using continous wavelet analysis for monitoring wheat yellow rust in different infestation stages based on unmanned aerial vehicle hyperspectral images. Applied Optics, v. 59, n. 26, p. 8003–8013, set. 2020.

ZHOU, X. et al. Predicting within-field variability in grain yield and protein content of winter wheat using UAV-based multispectral imagery and machine learning approaches. Plant Production Science, v. 24, n. 2, p. 137–151, abr. 2021.

ZHU, H. et al. UAV-based hyperspectral analysis and spectral indices constructing for quantitatively monitoring leaf nitrogen content of winter wheat. Applied Optics, v. 57, n. 27, p. 7722–7732, set. 2018.

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