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

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.

Plant phenotyping relevance

マルチスペクトル画像とX線画像を組み合わせ、種子の生理的品質（正常・異常発芽種子、死種子）を自動推定する手法の開発が中心であり、植物フェノタイピング方法に該当する。

Code and data availability

The article describes multispectral and X-ray imaging of Jatropha curcas seeds with PCA/CDA/nCDA/LDA analyses, but the supplied blocks contain no data availability statement, no public dataset or image repository, and no author code deposit or URL. The only URLs present are the article's own DOI, Creative Commons links

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