Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality

Vitor de Jesus Martins Bianchini, Gabriel Moura Mascarin, Lúcia Cristina Aparecida Santos Silva, Valter Arthur, Jens Michael Carstensen, Birte Boelt, Clíssia Barboza da Silva*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Background: The use of non-destructive methods with less human interference is of great interest in agricultural industry and crop breeding. Modern imaging technologies enable the automatic visualization of multi-parameter for characterization of biological samples, reducing subjectivity and optimizing the analysis process. Furthermore, the combination of two or more imaging techniques has contributed to discovering new physicochemical tools and interpreting datasets in real time. Results: We present a new method for automatic characterization of seed quality based on the combination of multispectral and X-ray imaging technologies. We proposed an approach using X-ray images to investigate internal tissues because seed surface profile can be negatively affected, but without reaching important internal regions of seeds. An oilseed plant (Jatropha curcas) was used as a model species, which also serves as a multi-purposed crop of economic importance worldwide. Our studies included the application of a normalized canonical discriminant analyses (nCDA) algorithm as a supervised transformation building method to obtain spatial and spectral patterns on different seedlots. We developed classification models using reflectance data and X-ray classes based on linear discriminant analysis (LDA). The classification models, individually or combined, showed high accuracy (> 0.96) using reflectance at 940 nm and X-ray data to predict quality traits such as normal seedlings, abnormal seedlings and dead seeds. Conclusions: Multispectral and X-ray imaging have a strong relationship with seed physiological performance. Reflectance at 940 nm and X-ray data can efficiently predict seed quality attributes. These techniques can be alternative methods for rapid, efficient, sustainable and non-destructive characterization of seed quality in the future, overcoming the intrinsic subjectivity of the conventional seed quality analysis.

Original languageEnglish
Article number9
JournalPlant Methods
Volume17
Issue number1
Number of pages13
ISSN1746-4811
DOIs
Publication statusPublished - Dec 2021

Bibliographical note

Funding Information:
This work was supported by Sao Paulo Research Foundation–FAPESP (Grant#2017/15220-7, Grant#2018/03802-4, Grant#2018/03807-6, Grant#2018/01774-3 and Grant#2019/04127-1).

Funding Information:
The authors gratefully acknowledge financial support from the Sao Paulo Research Foundation?FAPESP.

Publisher Copyright:
© 2021, The Author(s).

Keywords

  • Artificial intelligence
  • Jatropha curcas
  • Non-invasive methods
  • Radiographic images

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