Vitor de Jesus Martins Bianchini1, Gabriel Moura Mascarin2, Lúcia Cristina Aparecida Santos Silva3, Valter Arthur3, Jens Michael Carstensen4, Birte Boelt5, Clíssia Barboza da Silva6. 1. Department of Crop Science, College of Agriculture "Luiz de Queiroz", University of São Paulo, 11 Padua Dias Ave, Box 9, Piracicaba, SP, 13418-900, Brazil. 2. Laboratory of Environmental Microbiology, Brazilian Agricultural Research Corporation, Embrapa Environment, Rodovia SP 340, Km 127.5, Jaguariúna, 13820-000, Brazil. 3. Laboratory of Radiobiology and Environment, Center for Nuclear Energy in Agriculture, University of São Paulo, 303 Centenario Ave., Sao Dimas, Piracicaba, SP, 13416-000, Brazil. 4. Technical University of Denmark, 2800, Lyngby, Denmark. 5. Department of Agroecology, Science and Technology, Aarhus University, 4200, Slagelse, Denmark. 6. Laboratory of Radiobiology and Environment, Center for Nuclear Energy in Agriculture, University of São Paulo, 303 Centenario Ave., Sao Dimas, Piracicaba, SP, 13416-000, Brazil. clissia_usp@hotmail.com.
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.
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 automaticcharacterization 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.
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