Evaluating the Performance of Airborne and Ground Sensors for Applications in Precision Agriculture: Enhancing the Postprocessing State-of-the-Art Algorithm
Language English Country Switzerland Media electronic
Document type Journal Article
PubMed
36236792
PubMed Central
PMC9572098
DOI
10.3390/s22197693
PII: s22197693
Knihovny.cz E-resources
- Keywords
- NDVI, agriculture, gdal, pyQGIS, python, sensors,
- MeSH
- Algorithms * MeSH
- Image Processing, Computer-Assisted MeSH
- Agriculture * methods MeSH
- Publication type
- Journal Article MeSH
The main goals of the following paper are to evaluate the performance of two multispectral airborne sensors and compare their image data with in situ spectral measurements. Moreover, the authors aim to present an enhanced workflow for processing multitemporal image data using both commercial and open-source solutions. The research was provoked by the need for a relevant comparison between airborne and ground sensors for vegetation analysis and monitoring. The research team used an eBee fixed-wing platform and the multiSPEC 4c and Sequoia sensors. The authors carried out field measurements using a handheld spectrometer by Trimble-GreenSeeker. There were two flight campaigns which took place near the village of Tuhan in the Czech Republic. The results from the first campaign were discouraging, showing less possibility in the correlation between the aerial and field data. The second campaign resulted in a very high percentage of correlation between both types of data. The researchers present the image processing steps and their enhanced photogrammetric workflow for multitemporal data which helps experts and nonprofessionals to reduce their processing time.
See more in PubMed
Rouse J.W., Haas R.H., Schell J.A., Deering D.W., Harlan J.C. Monitoring the Vernal Advancement and Retrogradation (Greenwave Effect) of Natural Vegetation. Texas A & M University; College Station, TX, USA: 1974.
Yokota Y., Matsunaga T., Ohtake M., Haruyama J., Nakamura R., Yamamoto S., Ogawa Y., Morota T., Honda C., Saiki K., et al. Lunar photometric properties at wavelengths 0.5–1.6 μm acquired by SELENE Spectral Profiler and their dependency on local albedo and latitudinal zones. ICARUS. 2011;215:639–660. doi: 10.1016/j.icarus.2011.07.028. DOI
Yang C., Everitt J.H., Du Q., Luo B., Chanussot J. Using high-resolution airborne and satellite imagery to assess crop growth and yield variability for precision agriculture. Proc. IEEE. 2013;101:582–592. doi: 10.1109/JPROC.2012.2196249. DOI
Nebiker S., Lack N., Abächerli M., Läderach S. Light-Weight Multispectral Uav Sensors and Their Capabilities for Predicting Grain Yield and Detecting Plant Diseases. Int. Arch. Photogramm. Remote Sens. Spat. Sci. 2016;XLI-B1:963–970. doi: 10.5194/isprs-archives-XLI-B1-963-2016. DOI
Lukas V., Novák J., Neudert L., Svobodova I., Rodriguez-Moreno F., Edrees M., Kren J. The combination of UAV survey and Landsat imagery for monitoring of crop vigor in precision agriculture. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch. 2016;41:953–957. doi: 10.5194/isprs-archives-XLI-B8-953-2016. DOI
Starková H., Šedina J., Bílá Z. Monitoring of Heaps Using Various Technologies. Civ. Eng. J. 2005;2:11. doi: 10.14311/CEJ.2015.02.0011. DOI
Raeva P.L., Šedina J., Dlesk A. Monitoring of crop fields using multispectral and thermal imagery from UAV. Eur. J. Remote Sens. 2019;52:192–201. doi: 10.1080/22797254.2018.1527661. DOI
CRC Press Taylor & Francis Group . In: Precision Agriculture Technology for Crop Farming. Zhang Q., editor. CRC Press Taylor & Francis Group; Boca Raton, FL, USA: 2016.
Quille-Mamani J., Ruiz L.A., Ramos-Fernández L., Raeva P. Estimación de biomasa y rendimiento utilizando métricas fenológicas de imágenes Sentinel-2 en el cultivo de arroz: Caso de estudio Perú. 2022. [(accessed on 15 August 2022)]. Available online: http://cgat.webs.upv.es/wp-content/uploads/2022/09/XIX_Congreso_AET_actas-92-95.pdf.
Šedina J., Housarová E., Raeva P. Using RPAS for the detection of archaeological objects using multispectral and thermal imaging. Eur. J. Remote Sens. 2018;52:182–191. doi: 10.1080/22797254.2018.1562848. DOI
Boucher P. “You Wouldn’t have Your Granny Using Them”: Drawing Boundaries between Acceptable and Unacceptable Applications of Civil Drones. Sci. Eng. Ethics. 2015;22:1391–1418. doi: 10.1007/s11948-015-9720-7. PubMed DOI PMC
SESAR Joint Undertaking . European Drones Outlook Study. SESAR Joint Undertaking; Brussels, Belgium: 2016. p. 93. DOI
SenseFly Ltd. MultiSpec 4C CameraUser Manual. SenseFly Ltd.; Cheseaux-sur-Lausanne, Switzerland: 2017.
Lillesand T. Remote Sensing and Image Interpretation. 7th ed. John Wiley & Sons; Hoboken, NJ, USA: 2000.
MicaSense Overview of Agricultural Indices. [(accessed on 15 March 2022)]. Available online: https://support.micasense.com/hc/en-us/articles/227837307-Overview-of-Agricultural-Indices.
Cao S., Danielson B., Clare S., Koenig S., Campos-Vargas C., Sanchez-Azofeifa A. Radiometric calibration assessments for UAS-borne multispectral cameras: Laboratory and field protocols. ISPRS J. Photogramm. Remote Sens. 2019;149:132–145. doi: 10.1016/j.isprsjprs.2019.01.016. DOI
Poncet A.M., Knappenberger T., Brodbeck C., Fogle M., Shaw J.N., Ortiz B.V. Multispectral UAS data accuracy for different radiometric calibration methods. Remote Sens. 2019;11:1917. doi: 10.3390/rs11161917. DOI
Assmann J.J., Kerby J.T., Cunliffe A.M., Myers-Smith I.H. Vegetation monitoring using multispectral sensors—Best practices and lessons learned from high latitudes. J. Unmanned Veh. Syst. 2019;7:54–75. doi: 10.1139/juvs-2018-0018. DOI
Parrot Sequoia—The Revolutionary Multispectral Sensor. [(accessed on 23 September 2022)]. Available online: https://www.parrot.com/en/shop/accessories-spare-parts/other-drones/sequoia.
Carron C. A market Leader in Civilian Drones Joins Sensefly and Pix4D. EPFL; Lausanne, Switzerland: 2012. [(accessed on 14 March 2022)]. Available online: https://actu.epfl.ch/news/a-market-leader-in-civilian-drones-joins-sensefly-/
Trimble . GreenSeeker Handheld Crop Sensor. Trimble Agriculture; Sunnyvale, CA, USA: 2022. [(accessed on 8 May 2022)]. Available online: https://agriculture.trimble.com/product/greenseeker-handheld-crop-sensor/
Pix4D pix4Dmapper 2022. [(accessed on 15 August 2022)]. Available online: www.pix4d.com.
Luhmann T., Robson S., Kyle S., Boehm J. Close-Range Photogrammetry and 3D Imaging. De Gruyter; Berlin, Germany: 2014. [(accessed on 15 August 2022)]. Available online: https://encore2.lsbu.ac.uk/iii/encore/record/C__Rb2773743__S3dphotogrammetry__Orightresult__U__X2?lang=eng&suite=cobalt.
Support P. Exif/Xmp Tags for Radiometric Correction. [(accessed on 14 March 2022)]. Available online: https://support.pix4d.com/hc/en-us/articles/115001846106-Exif-Xmp-tags-for-radiometric-correction.
Martinoli A., Mondada F., Correll N., Mermoud G. Structure from Motion Using the Extended Kalman Filter. Volume 83. Springer; Berlin/Heidelberg, Germany: 2012.
Python Software Foundation Python Language Reference. 2022. [(accessed on 21 April 2022)]. Available online: https://www.python.org/
Raeva P., Pavelka K. Optimized post-processing of multiple UAV images for forestry inspections. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch. 2020;43:15–19. doi: 10.5194/isprs-archives-XLIII-B1-2020-15-2020. DOI
PgAdmin PostgreSQL Tools. 2022. [(accessed on 21 March 2022)]. Available online: https://www.pgadmin.org/
Everitt B.S. The Cambridge Dictionary of Statistics. Cambridge University Press; Cambridge, UK: 1998.