Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Detection of Defects, Infection and Seed Health <s> There is hardly a more efficient means of spreading plant diseases over long distances than by transportation of seed, and practically no limit as to how far seed may be sent. Any seed-borne inoculum surviving from harvest to time of sowing may be safely conveyed from any part of the world to another, irrespective of geographical barriers such as oceans and mountains. This is true for any part of plants transported over long distances, such as vegetative plant material for propagation. But from several points of view there is a particular risk attached to seed. <s> BIB001 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Detection of Defects, Infection and Seed Health <s> Sorting systems based on optical methods have the potential to rapidly detect and physically remove seeds severely contaminated by fungi, or infested internally by insect larvae or pupae. Thus, the literature on sorting systems based on optical methods for detecting and sorting seeds with these attributes was reviewed. Sorting indices based on wavelengths useful for detecting these attributes were emphasized. Surface characteristics of seeds, like discoloration caused by fungi, are generally detectable in the visible range of the electromagnetic spectrum, whereas internal attributes are detectable in the near-infrared range. The spectral differences between sound and infested seeds are usually subtle, but full-spectrum and two-wavelength classification models have succeeded in detecting and classifying seeds based on these attributes. For high sorting accuracies, wavelength identification and proper selection of a sorting criterion are important. Color, chitin, ergosterol, or hydrolysis of triglycerides h... <s> BIB002 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Detection of Defects, Infection and Seed Health <s> du Toit, L. J., and Hernandez-Perez, P. 2005. Efficacy of hot water and chlorine for eradication of Cladosporium variabile, Stemphylium botryosum, and Verticillium dahliae from spinach seed. Plant Dis. 89:1305-1312. Cladosporium variabile, Stemphylium botryosum, and Verticillium dahliae are seedborne and seed-transmitted pathogens of spinach. Spinach seed treatments in 1.2% NaOCl for 10 to 60 min, or hot water (40, 45, 50, 55, and 60oC) for 10 to 40 min, were evaluated for eradication of these fungi from seed. C. variabile and V. dahliae were largely eradicated by chlorine treatment for ≥10 min. Although chlorine treatment reduced the incidence of S. botryosum, this fungus was not eradicated after 60 min in chlorine. Seed germination was not affected adversely by chlorine treatment, even after 60 min. In contrast, germination was reduced significantly by hot water treatment at 50oC for ≥30 min or 55 or 60oC for ≥10 min. C. variabile was eradicated from seed treated in 40oC water for 10 min. V. dahliae was eradicated from seed treated at 55oC for ≥30 min or 60oC for ≥10 min. S. botryosum was eradicated from a lightly infected seed lot (5% incidence) by hot water treatment at 55 or 60oC for ≥10 min, but could not be eradicated from two heavily infected lots (>65% incidence), even at 60oC for 40 min. Using precisely controlled parameters, chlorine or hot water seed treatments can be used to eradicate C. variabile and reduce the incidence of S. botryosum and V. dahliae in spinach seed without damaging germination. <s> BIB003 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Detection of Defects, Infection and Seed Health <s> Highlights? Aflatoxin contaminated food items can be detected by multispectral imaging. ? Aflatoxin level of the food lot can be decreased by elimination of the detected ones. ? Discriminative features in images were extracted by 3D LDB based algorithm. ? Fusion in data level describes the characteristics of multispectral imaging system. Mycotoxins are the toxic metabolites of certain filamentous fungi and have been demonstrated to cause various health problems in humans, including immunosuppression and cancer. Among them, the aflatoxins have received greater attention because they are potent carcinogens and are responsible for many human deaths per annum, mostly in non-industrialized countries. Various regulatory agencies have enforced limits on the concentrations of these toxins in foods and feeds involved in international commerce. Hyperspectral and multispectral imaging are becoming increasingly important for rapid and nondestructive testing for the presence of such contaminants. However, the high number of spectral bands needed may render such image acquisition systems too complex, expensive and slow. Moreover, they tend to generate overwhelming amount of data, making effective processing of this information in real time difficult. In this study, a two-dimensional local discriminant bases algorithm was developed to detect the location of the discriminative features in the multispectral data space. The algorithm identifies the optimal passband width and center frequencies of optical filters to be used for a multispectral imaging system. This was applied to a multispectral imaging system used to detect aflatoxin-contaminated hazelnut kernels and red chili peppers. Classification accuracies of 92.3% and 80% were achieved for aflatoxin-contaminated and uncontaminated hazelnuts and red chili peppers, respectively. The aflatoxin concentrations were decreased from 608 to 0.84ppb for tested hazelnuts and from 38.26 to 22.85ppb for red chili peppers by removal of the nuts/peppers that were classified as aflatoxin-contaminated. The algorithm was also used to classify fungal contaminated and uncontaminated hazelnut kernels, and an accuracy of 95.6% was achieved for this broader classification. <s> BIB004 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Detection of Defects, Infection and Seed Health <s> Germination vigor is driven by the ability of the plant embryo, embedded within the seed, to resume its metabolic activity in a coordinated and sequential manner. Studies using "-omics" approaches support the finding that a main contributor of seed germination success is the quality of the messenger RNAs stored during embryo maturation on the mother plant. In addition, proteostasis and DNA integrity play a major role in the germination phenotype. Because of its pivotal role in cell metabolism and its close relationships with hormone signaling pathways regulating seed germination, the sulfur amino acid metabolism pathway represents a key biochemical determinant of the commitment of the seed to initiate its development toward germination. This review highlights that germination vigor depends on multiple biochemical and molecular variables. Their characterization is expected to deliver new markers of seed quality that can be used in breeding programs and/or in biotechnological approaches to improve crop yields. <s> BIB005 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Detection of Defects, Infection and Seed Health <s> Abstract The objective of this study was to evaluate the safety quality of wheat kernels and more specifically the infection of wheat by Fusarium culmorum. We have developed a new non-destructive methodology based on multispectral imaging and chemometrics to detect wheat contamination. This method has been applied to evaluate the tolerance to Fusarium Head Blight (FHB) on six accessions of durum wheat. Two of the accessions are parental lines, one susceptible and one resistant to Fusarium, and the other four are their offspring lines. Multispectral images of dorsal and ventral sides of all kernels were acquired with an in-house imaging system and processed with chemometrical tools, including principal component analysis (PCA) and multiple linear regression (MLR). According to the quantitative PCR (qPCR) results, PCA was able to detect infested kernels and to identify affected areas with kernels. Moreover, regression analysis of the images allowed the degree of contamination for each pixel to be estimated, and thus mapping of contamination for each kernel. <s> BIB006 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Detection of Defects, Infection and Seed Health <s> Multispectral imaging with object-wise multivariate image analysis was evaluated for its potential to grade whole white maize kernels. The types of defective materials regarded in grading legislation were divided into 13 classes, and were imaged with a multispectral imaging instrument spanning the UV, visible and NIR regions (19 wavelengths ranging from 375 to 970nm). Object-wise partial least squares discriminant analysis (PLS-DA) models were developed and validated with an independent data set. Results demonstrated good performance in distinguishing between sound maize and undesirable materials, with cross-validated coefficients of determination (Q2) and classification accuracies ranging from 0.35 to 0.99 and 83 to 100%, respectively. Wavelengths related to absorbance of green, yellow and orange colour indicated the presence of lycopene and anthocyanin (505, 525, 570 and 590 nm). NIR wavelengths 890, 940 nm (associated with fat) and 970 nm (associated with water) were generally identified as important features throughout the study. <s> BIB007
Seed health is directly affected by any damage occurred to the seeds as well as the invasion of disease and insect infestation. Seed damage may result from poor harvesting and storage conditions; from rough handling during cleaning and processing or even from disease and/or insect infestations. Such damages may be external, such as cracks in the seed coat or splitting of the seed which may also lead to internal damages and sometimes germinating and vigor losses BIB005 . Beside losing the germination capacity and producing abnormal seedling growth, infections caused by fungi and viruses usually led to serious problems for seed growers and producers. Therefore, many countries have stipulated strict quarantine rules for importing seeds from zones where specific infections may be endemic BIB001 BIB003 . Consequently, it is very crucial to detect the infection level of the seeds accurately right after harvest and to optimize methods to reduce the infection on the seeds before sowing. Seed health tests are often performed on representative samples of seed lots, but availability of automatic system to analyze seed lots in individual-seed basis would be advantageous For instance, detection of fungi on seed is performed usually by inspection of the dry seed, washing tests, incubation methods, embryo count method or seedling symptom tests, but employing a rapid, non-destructive method would help in saving time and cost spent compared to the ordinary evaluation methods. Although the International Seed Testing Association (ISTA) has developed accredited protocols for seed health testing of many species, there is no accredited protocol for seed health testing for all species  . Thus, development of a high-speed system based on optical methods having the potential to rapidly detect and physically remove seeds severely diseased or contaminated by fungi, or infested internally by insect larvae or pupae is very crucial BIB002 . In this regard, multispectral imaging has found its way towards automating the inspection and evaluation practices of seed health and proved to be useful in the separation of infected seeds from uninfected seeds in several species BIB006   . As seed health tests are time consuming processes and require substantial training for characterization of pathogenic fungi on seed, multispectral imaging system (395-970 nm) was used by Olesen et al.  for identifying surface properties of different fungal infections in spinach seeds and for discrimination among healthy and seeds infected seeds with different Fusarium strains (Verticillium spp., Fusarium spp., Stemphylium botryosum, Cladosporium spp. and Alternaria alternate). They used canonical discriminant analysis (CDA) for separation of image pixels based on their mean intensity and Jefferies-Matusita (JM) distance for classification and modeling spectral data which gave a separation accuracy of 26-88% between uninfected and Fusarium spp. infected seeds. Vrešak et al.  utilized a multispectral imaging system (375-970 nm) with 19 wavelength bands to discriminate 27 winter wheat (Triticum aestivum L.) varieties and nine triticale (Triticosecale Wittm. & Camus) varieties in terms of their resistance to fungal infection. The same method outlined by Olesen et al.  was followed in which the nCDA method for multispectral images analysis was used to perform the data transformation on all 19 bands. In this method, some regions of interest (ROIs) representing the infected and uninfected (healthy) tissues of the seeds based on the visual signs of infection or unification status on dry seeds were marked and their reference spectral patterns were used in training the model during data transformation. The CDA transformation was used to minimize the distance between observations within the group and maximize distance between the known groups (36 different varieties or/and infected and uninfected parts of the seeds). The supervised transformation by nCDA was used to divide the images into regions of interest. To determine the accuracy of detection, the nCDA transformation was then applied on all images and examined whether the pixel classification results corresponded to the manual visualization. Classification results shown in Figure 7 illustrate how accurate the method was in identifying infection groups in which the parts infected with Alternania sp. appeared in orange and parts infected with Fusarium sp. appeared as light blue; meanwhile, the uninfected healthy parts appeared in dark blue. maximize distance between the known groups (36 different varieties or/and infected and uninfected parts of the seeds). The supervised transformation by nCDA was used to divide the images into regions of interest. To determine the accuracy of detection, the nCDA transformation was then applied on all images and examined whether the pixel classification results corresponded to the manual visualization. Classification results shown in Figure 7 illustrate how accurate the method was in identifying infection groups in which the parts infected with Alternania sp. appeared in orange and parts infected with Fusarium sp. appeared as light blue; meanwhile, the uninfected healthy parts appeared in dark blue. In another study for detecting different defects in maize kernels, Sendin et al. BIB007 used a multispectral imaging system in the UV, visible and NIR regions (375-970 nm) with 19 discrete wavelengths. They used principle component analysis (PCA) to explore spectral variation among samples and to visualize the mean difference between sound and defected seeds. The defects include damages from heat or water, fungal damage from Fusarium or Diplodia; pinked white or yellow maize, broken kernels; and foreign matters (wheat, soy, sunflower seeds, sorghum and maize plant material). Moreover, partial least squares discriminant analysis (PLS-DA) models were used for distinguishing between sound maize and undesirable materials. The PLS-DA model had good classification accuracies ranging from 83% to 100%. In another study for detecting different defects in maize kernels, Sendin et al. BIB007 used a multispectral imaging system in the UV, visible and NIR regions (375-970 nm) with 19 discrete wavelengths. They used principle component analysis (PCA) to explore spectral variation among samples and to visualize the mean difference between sound and defected seeds. The defects include damages from heat or water, fungal damage from Fusarium or Diplodia; pinked white or yellow maize, broken kernels; and foreign matters (wheat, soy, sunflower seeds, sorghum and maize plant material). Moreover, partial least squares discriminant analysis (PLS-DA) models were used for distinguishing between sound maize and undesirable materials. The PLS-DA model had good classification accuracies ranging from 83% to 100%. The study also concluded that wavelengths related to red, pink and blue carotenoid and anthocyanin pigments (505, 525, 570 and 590 nm), and NIR wavelengths 890, 940 nm (associated with fat) and 970 nm (associated with water) are considered as relevant wavelengths for discrimination. Figure 8a shows the PCA score image to demonstrate how the heat damaged seeds are entirely different from the sound seeds. By using supervised discrimination method by PLS-DA, the discrimination among heat damage and sound maize kernels was very obvious and easier to observe (Figure 8b ). This result reveals high potential to industrial applications due to shorter time used in image acquisition and analysis as well as lower cost compared with hyperspectral imaging systems. seed incubation (adapted from Vrešak et al.  ). In another study for detecting different defects in maize kernels, Sendin et al. BIB007 used a multispectral imaging system in the UV, visible and NIR regions (375-970 nm) with 19 discrete wavelengths. They used principle component analysis (PCA) to explore spectral variation among samples and to visualize the mean difference between sound and defected seeds. The defects include damages from heat or water, fungal damage from Fusarium or Diplodia; pinked white or yellow maize, broken kernels; and foreign matters (wheat, soy, sunflower seeds, sorghum and maize plant material). Moreover, partial least squares discriminant analysis (PLS-DA) models were used for distinguishing between sound maize and undesirable materials. The PLS-DA model had good classification accuracies ranging from 83% to 100%. For the detection of fungal contamination, Jaillais et al. BIB006 designed and developed a non-destructive methodology based on LED-illuminated multispectral imaging and chemometric to detect Fusarium head blight infection in wheat kernels. For each sample, eight RGB images with a spatial resolution of 524 × 702 pixels each were acquired and merged into an "image-cube" dimensioned 524 × 702 × 24. Spectral data were extracted from every single kernel and then analyzed via PCA and stepwise multilinear regression (SMLR) methods. The developed SMLR model was then applied to estimate the degree of contamination of each pixel in the image to produce 'predicted' classification images with high values indicating a high local contamination. The PCA results of the data indicated the possibility of separating the 'susceptible' and 'resistant' genotypes of wheat kernels (Figure 9a,b) . Similarly, SMLR models applied on multivariate images provide 'predicted' classification images of the wheat kernels. In such classification image, the vales of the pixels are coded based on their predicted degree of contamination. Based on the visualization of contamination in this image, a kernel with high contamination level exhibited more red pixels indicating that the 'susceptible' kernels had a majority of red pixels while 'resistant' ones exhibited more blue pixels (Figure 9c,d) . The study also concluded that wavelengths related to red, pink and blue carotenoid and anthocyanin pigments (505, 525, 570 and 590 nm), and NIR wavelengths 890, 940 nm (associated with fat) and 970 nm (associated with water) are considered as relevant wavelengths for discrimination. Figure 8a shows the PCA score image to demonstrate how the heat damaged seeds are entirely different from the sound seeds. By using supervised discrimination method by PLS-DA, the discrimination among heat damage and sound maize kernels was very obvious and easier to observe (Figure 8b ). This result reveals high potential to industrial applications due to shorter time used in image acquisition and analysis as well as lower cost compared with hyperspectral imaging systems. For the detection of fungal contamination, Jaillais et al. BIB006 designed and developed a nondestructive methodology based on LED-illuminated multispectral imaging and chemometric to detect Fusarium head blight infection in wheat kernels. For each sample, eight RGB images with a spatial resolution of 524 × 702 pixels each were acquired and merged into an "image-cube" dimensioned 524 × 702 × 24. Spectral data were extracted from every single kernel and then analyzed via PCA and stepwise multilinear regression (SMLR) methods. The developed SMLR model was then applied to estimate the degree of contamination of each pixel in the image to produce 'predicted' classification images with high values indicating a high local contamination. The PCA results of the data indicated the possibility of separating the 'susceptible' and 'resistant' genotypes of wheat kernels (Figures 9a and 9b) . Similarly, SMLR models applied on multivariate images provide 'predicted' classification images of the wheat kernels. In such classification image, the vales of the pixels are coded based on their predicted degree of contamination. Based on the visualization of contamination in this image, a kernel with high contamination level exhibited more red pixels indicating that the 'susceptible' kernels had a majority of red pixels while 'resistant' ones exhibited more blue pixels (Figures 9c,d ). Figure 9 . PCA score-images issued from PCA on whole images for the (a) resistant and (b) susceptible wheat genotypes; and the predicted contamination images resulting from SMLR model in the (c) resistant and (d) susceptible wheat genotypes kernels in which a kernel highly contaminated have a lot of red pixels (adapted from Jaillais et al. BIB006 ). Because mycotoxins, the toxic metabolites of certain filamentous fungi, cause very severe health problems and are considered as potent carcinogens causing many human deaths per annum, Kalkan et al. BIB004 applied multispectral imaging for detecting aflatoxin-contaminated hazelnut kernels and chili pepper. The system was illuminated by UV-A light having a peak intensity at 365 nm and supported with a filter wheel for holding 12 different band-pass filters with 10 nm full width half maximum (FWHM) in the range of 400-510 nm and two other filters at 550 and 600 nm with 70 and 40 nm FWHM, respectively. By using an algorithm of local discriminant bases (LDB) to Figure 9 . PCA score-images issued from PCA on whole images for the (a) resistant and (b) susceptible wheat genotypes; and the predicted contamination images resulting from SMLR model in the (c) resistant and (d) susceptible wheat genotypes kernels in which a kernel highly contaminated have a lot of red pixels (adapted from Jaillais et al. BIB006 ). Because mycotoxins, the toxic metabolites of certain filamentous fungi, cause very severe health problems and are considered as potent carcinogens causing many human deaths per annum, Kalkan et al. BIB004 applied multispectral imaging for detecting aflatoxin-contaminated hazelnut kernels and chili pepper. The system was illuminated by UV-A light having a peak intensity at 365 nm and supported with a filter wheel for holding 12 different band-pass filters with 10 nm full width half maximum (FWHM) in the range of 400-510 nm and two other filters at 550 and 600 nm with 70 and 40 nm FWHM, respectively. By using an algorithm of local discriminant bases (LDB) to obtain localized information for spectral signals and image classification, the detection accuracy of aflatoxin-contaminated red peppers and hazelnut kernels was 92.3 and 79.17%, respectively. More precise overall classification accuracy of 95.67% was achieved in classifying hazelnuts as fungal infested kernels without considering their aflatoxin concentrations.