国外科学家最近利用Videometer多光谱成像系统发表了题为Integrating Optical Imaging Tools for Rapid and Non-invasive Characterization of Seed Quality: Tomato (Solanum lycopersicum L.) and Carrot (Daucus carota L.) as Study Cases的文章，结论是，从实际应用上来说，此类技术和方法可用于农业和工业领域低品质种子筛选。文章涉及到Videometer部分，Multispectral images were captured at 19 wavelengthss365 (UVA), 405 (violet), 430 (indigo), 450 (blue), 470 (blue), 490 (cyan), 515 (green), 540 (green), 570 (yellow), 590 (amber), 630 (red), 645 (red), 660 (red), 690 (deep red), 780 (deep red), 850, 880, 940, and 970 nm (the last four wavelengths in the NIR region), using a VideometerLab4instrument (Videometer A/S, Herlev, Denmark) and its software (version 3.14.9). This system can capture multispectral images combining them into high-resolution multispectral images (2192 × 2192 pixels）。

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Integrating Optical Imaging Tools for Rapid and Non-invasive Characterization of Seed Quality: Tomato (Solanum lycopersicum L.) and Carrot (Daucus carota L.) as Study Cases

Patrícia A. Galletti1, Marcia E. A. Carvalho2, Welinton Y. Hirai3, Vivian A. Brancaglioni3, Valter Arthur4 and Clíssia Barboza da Silva4*

1Department of Crop Science, College of Agriculture “Luiz de Queiroz”, University of São Paulo, Piracicaba, Brazil

2Department of Genetics, College of Agriculture “Luiz de Queiroz”, University of São Paulo, Piracicaba, Brazil

3Department of Exacts Sciences, College of Agriculture “Luiz de Queiroz”, University of São Paulo, Piracicaba, Brazil

4Laboratory of Radiobiology and Environment, Center for Nuclear Energy in Agriculture, University of São Paulo, Piracicaba, Brazil

Light-based methods are being further developed to meet the growing demands for food in the agricultural industry. Optical imaging is a rapid, non-destructive, and accurate technology that can produce consistent measurements of product quality compared to conventional techniques. In this research, a novel approach for seed quality prediction is presented. In the proposed approach two advanced optical imaging techniques based on chlorophyll fluorescence and chemometric-based multispectral imaging were employed. The chemometrics encompassed principal component analysis (PCA) and quadratic discrimination analysis (QDA). Among plants that are relevant as both crops and scientific models, tomato, and carrot were selected for the experiment. We compared the optical imaging techniques to the traditional analytical methods used for quality characterization of commercial seedlots. Results showed that chlorophyll fluorescence-based technology is feasible to discriminate cultivars and to identify seedlots with lower physiological potential. The exploratory analysis of multispectral imaging data using a non-supervised approach (two-component PCA) allowed the characterization of differences between carrot cultivars, but not for tomato cultivars. A Random Forest (RF) classifier based on Gini importance was applied to multispectral data and it revealed the most meaningful bandwidths from 19 wavelengths for seed quality characterization. In order to validate the RF model, we selected the five most important wavelengths to be applied in a QDA-based model, and the model reached high accuracy to classify lots with high-and low-vigor seeds, with a correct classification from 86 to 95% in tomato and from 88 to 97% in carrot for validation set. Further analysis showed that low quality seeds resulted in seedlings with altered photosynthetic capacity and chlorophyll content. In conclusion, both chlorophyll fluorescence and chemometrics-based multispectral imaging can be applied as reliable proxies of the physiological potential in tomato and carrot seeds. From the practical point of view, such techniques/methodologies can be potentially used for screening low quality seeds in food and agricultural industries.