Skip to main content
SpringerLink
Log in

A Method for Multispectral Images Alignment at Different Heights on the Crop

  • Conference paper
  • First Online:
15th International Congress on Agricultural Mechanization and Energy in Agriculture (ANKAgEng 2023)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 458))

  • 59 Accesses

Abstract

Multispectral imaging (MSI) for agricultural applications is playing a key role in plant stress assessment. However, one of the main problems is the misalignment of spectral bands provided by these instruments. The approach proposed in this study considers various distances from the target (500 mm to 1500 mm with a step size of 100 mm) and applies corrective shifts to achieve accurate registration among bands. Through a comparative evaluation of two alignment methods, Checkerboard (CB) and Discrete Fourier Transform (FT), this research aims to provide an effective solution for accurate image registration by facilitating reliable spectral analysis. Specifically, the proposed method involved the analysis of alignment-related offsets among the tested methods. In addition, the study explored the extraction of vegetation spectral indices for vegetation analysis and discrimination between healthy and diseased plants and evaluated their relationship with the quality of alignment obtained at different heights. The results confirmed the trends in the changes in offsets as the target distance varies, showing satisfactory accuracy in the alignment of raw spectral images at different distances, with an error of about 1 pixel. Among the vegetation indices used, the Normalized Difference Vegetation Index (NDVI) proved to be capable of discriminating between healthy and nonhealthy leaves. The study aims to establish a framework applicable to remote sensing and agricultural monitoring, providing a valuable tool for monitoring plant health.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Stafford JV (2000) Implementing precision agriculture in the 21st century. J Agric Eng Res 76(3):267–275

    Article  Google Scholar 

  2. Saddik A, Latif R, El Ouardi A, Elhoseny M, Khelifi A (2022) Computer development based embedded systems in precision agriculture: tools and application. 72(1):589–611

    Google Scholar 

  3. Mahlein AK (2016) Plant disease detection by imaging sensors – parallels and specific demands for precision agriculture and plant phenotyping. Plant Disease. American Phytopathological Society

    Google Scholar 

  4. Xu J, Mishra P (2022) Combining deep learning with chemometrics when it is really needed: a case of real time object detection and spectral model application for spectral image processing. Anal Chim Acta 1202:339668

    Article  CAS  Google Scholar 

  5. Omia E et al (2023) Remote sensing in field crop monitoring: a comprehensive review of sensor systems, data analyses and recent advances. Remote Sens 15(2):354

    Article  Google Scholar 

  6. Yasir R, Eramian M, Stavness I, Shirtliffe S, Duddu H (2018) Data-driven multispectral image registration. In: Proceedings of 2018 15th conference on computer and robot vision, CRV 2018, pp 230–237

    Google Scholar 

  7. Mishra P, Asaari MSM, Herrero-Langreo A, Lohumi S, Diezma B, Scheunders P (2017) Close range hyperspectral imaging of plants: a review. Biosys Eng 164:49–67

    Article  Google Scholar 

  8. Hassanpour M, Dadras Javan F, Azizi A (2019) Band to band registration of multi-spectral aerial imagery-relief displacement and miss-registration error. Int Arch Photogram Remote Sens Spat Inf Sci - ISPRS Arch 42(4/W18):467–474

    Google Scholar 

  9. Banerjee BP, Anuray S, Patrick R, Cullen J, Raval A, Cullen PJ (2018). Alignment of UAV-hyperspectral bands using keypoint descriptors in a spectrally complex environment. 9(6):524–533

    Google Scholar 

  10. Zhang A et al (2021) A handheld grassland vegetation monitoring system based on multispectral imaging. Agriculture 11(12):1262

    Article  Google Scholar 

  11. Qin J, Chao K, Kim MS, Lu R, Burks TF (2013) Hyperspectral and multispectral imaging for evaluating food safety and quality. J Food Eng 118(2):157–171

    Article  CAS  Google Scholar 

  12. Ren X, Sun M, Zhang X, Liu L (2017) A simplified method for UAV multispectral images mosaicking. Remote Sens 9(9):962

    Article  Google Scholar 

  13. Hong G, Zhang Y (2007). Combination of feature-based and area-based image registration technique for high resolution remote sensing image. In: International geoscience and remote sensing symposium (IGARSS), pp 377–380

    Google Scholar 

  14. Zitová B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21(11):977–1000

    Article  Google Scholar 

  15. Feng R, Du Q, Li X, Shen H (2019) Robust registration for remote sensing images by combining and localizing feature- and area-based methods. ISPRS J Photogramm Remote Sens 151:15–26

    Article  Google Scholar 

  16. Zhang X, Leng C, Hong Y, Pei Z, Cheng I, Basu A (2021) Multimodal remote sensing image registration methods and advancements: a survey. Remote Sens 13(24):5128

    Article  Google Scholar 

  17. Firmenichy D, Brown M, Süsstrunk, S (2011) Multispectral interest points for RGB-NIR image registration, pp 181–184

    Google Scholar 

  18. Ron B, Ohad B-S, Yael E, Marko H (2014) Image registration for agricultural sensing tasks. cs.bgu.ac.ilB Ron, BS Ohad, E Yael, H Markocs.bgu.ac.il. Repéré à https://www.cs.bgu.ac.il/~obs/Publications/2014Berenstein_Ben_Shahar_Edan_Godesa_Hacevar-Image_Registration_for_Agriculture_Spraying_Tasks.pdf

  19. Richter R, Kellenberger T, Kaufmann H (2009) Comparison of topographic correction methods. Remote Sens 1:184–196

    Article  Google Scholar 

  20. Scaioni M, Barazzetti L, Gianinetto M (2018) Multi-image robust alignment of medium-resolution satellite imagery. Remote Sens 10(12):1969

    Article  Google Scholar 

  21. Syrris V, Ferri S, Ehrlich D, Pesaresi M (2015) Image enhancement and feature extraction based on low-resolution satellite data. IEEE J Sel Top Appl Earth Obs Remote Sens 8:1986–1995

    Article  Google Scholar 

  22. Krus A, Valero C, Ramirez J, Cruz C, Barrientos A, del Cerro J (2021) Distortion and mosaicking of close-up multi-spectral images. In: Precision agriculture 2021, pp. 33–46

    Google Scholar 

  23. Zhang Z (2000) A flexible new technique for camera calibration. IEEE Trans Pattern Anal Mach Intell 22(11):1330–1334

    Article  Google Scholar 

  24. Heikkila J, Silven O (1997) A four-step camera calibration procedure with implicit image correction. In: IEEE international conference on computer vision and pattern recognition

    Google Scholar 

  25. Bouguet JY. Camera calibration toolbox for matlab. Computational Vision at the California Institute of Technology

    Google Scholar 

  26. Bradski G, Kaehler A (2008) Learning OpenCV: computer vision with the OpenCV library. O’Reilly, Sebastopol

    Google Scholar 

  27. Scaramuzza D, Martinelli A, Siegwart R (2006) A flexible technique for accurate omnidirectional camera calibration and structure from motion. In: Proceedings of IEEE international conference of vision systems (ICVS 2006), New York, 5–7 January

    Google Scholar 

  28. Scaramuzza D, Martinelli A, Siegwart R (2006) A toolbox for easy calibrating omnidirectional cameras. In: Proceedings to IEEE international conference on intelligent robots and systems (IROS 2006), Beijing China, 7–15 October

    Google Scholar 

  29. Rufli M, Scaramuzza D, Siegwart R (2008) Automatic detection of checkerboards on blurred and distorted images. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS 2008), Nice, France, 22–26 September

    Google Scholar 

  30. Geiger A, Moosmann F, Car Ö, Schuster B (2012) Automatic camera and range sensor calibration using a single shot. In: Proceedings - IEEE international conference on robotics and automation, pp 3936–3943

    Google Scholar 

  31. Guizar-Sicairos M, Thurman ST, Fienup JR (2008) Efficient subpixel image registration algorithms. Opt Lett 33(2):156

    Article  Google Scholar 

  32. Haboudane D, Miller JR, Pattey E, Zarco-Tejada PJ, Strachan IB (2004) Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens Environ 90(3):337–352

    Article  Google Scholar 

  33. Bannari A, Morin D, Bonn F, Huete AR (1995) A review of vegetation indices. Remote Sens Rev 13(1–2):95–120

    Article  Google Scholar 

  34. Xue J, Su B (2017) Significant remote sensing vegetation indices: a review of developments and applications. J Sens 2017:1–17

    Article  CAS  Google Scholar 

  35. Schell JA (1973) Monitoring vegetation systems in the great plains with ERTS. NASA Spec. Publ. 351:309

    Google Scholar 

  36. Daughtry CS, Walthall CL, Kim MS, De Colstoun EB, McMurtrey Iii JE (2000) Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens Environ 74:229–239

    Article  Google Scholar 

  37. Zhang D, Wang Q, Lin F, Yin X, Gu C, Qiao H (2020) Development and evaluation of a new spectral disease index to detect wheat fusarium head blight using hyperspectral imaging. Sensors (Basel) 20(8):2260

    Article  Google Scholar 

Download references

Funding

This study was carried out within the Agritech National Research Center and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4 – D.D. 1032 17/06/2022, CN00000022). This manuscript reflects only the authors’ views and opinions, neither the European Union nor the European Commission can be considered responsible for them.

Author information

Authors and Affiliations

  1. School SAFE, University of Basilicata, Viale dell’Ateneo Lucano 10, 85100, Potenza, Italy

    Sabina Laveglia & Giuseppe Altieri

Authors
  1. Sabina Laveglia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Giuseppe Altieri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sabina Laveglia .

Editor information

Editors and Affiliations

  1. National Research Council of Italy, Torino, Italy

    Eugenio Cavallo

  2. Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso, Chile

    Fernando Auat Cheein

  3. Dipartimento TESAF (University of Padova, TESAF Department), Università di Padova, Legnaro, Italy

    Francesco Marinello

  4. Department of Agricultural Machinery and Technologies Engineering, Ankara University, Altındağ/Ankara, Türkiye

    Kamil Saçılık

  5. Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD, USA

    Kasiviswanathan Muthukumarappan

  6. Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, Uttar Pradesh, India

    Purushothaman C. Abhilash

Ethics declarations

The authors declare no conflict of interest.

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Laveglia, S., Altieri, G. (2024). A Method for Multispectral Images Alignment at Different Heights on the Crop. In: Cavallo, E., Auat Cheein, F., Marinello, F., Saçılık, K., Muthukumarappan, K., Abhilash, P.C. (eds) 15th International Congress on Agricultural Mechanization and Energy in Agriculture. ANKAgEng 2023. Lecture Notes in Civil Engineering, vol 458. Springer, Cham. https://doi.org/10.1007/978-3-031-51579-8_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-51579-8_36

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-51578-1

  • Online ISBN: 978-3-031-51579-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation