Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Seed Purity and Variety Discrimination <s> ABSTRACT This paper combines multispectral imaging and simple image processing techniques for the non-destructive identification of Thai rice breeds. Especially, we exploit only two fluorescent wavelengths in a 500-580 nm wavelength band and utilize simple image thresholding, blob filtering, and blob analysis techniques in order to identify 8 different Thai rice breeds. Other key features in clude no waste produced and fast identifi cation time. In our experimental study, UVC light is used as our exciting light, a liquid crystal tunable optical filter is used for our wavelength selection, and a camera with 644 u 488 active pixels is used to capture desired wavelength images. Milled rice grains from 8 different Thai rice breeds having similar size and shape are tested. Th ere is also one glutinous rice breed in our experiment. Our experimental result shows that by suitably applying image thresholding, blob filtering, and blob analysis to fluorescent images, all Thai rice breeds ca n be effectively identified. Keywords: Rice, Multispectral imaging, Hyperspectral imaging, Fluorescent imaging, Image processing, Nondestructive testing, Agri-photonics. <s> BIB001 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Seed Purity and Variety Discrimination <s> Abstract: The commercial value of small grains such as wheat and barley is determined by their overall quality. Grading systems established by the major countries exporting these grains establish maximum tolerances for various contaminants and damage factors to the grain kernels. Grading factors are mainly visual characteristics. Collectively, the grading factors describe grain quality and safety as affected by growing conditions, handling and storage practices. Human visual inspection is the current method of grain quality assessment, which can be subjective and inconsistent. Alternative instrumental approaches to quality assurance are constantly being researched. Hyperspectral imaging has been used effectively for the detection and quantification of grain damage due to various grading factors such as mildew, fusarium, sprouting and green immature seeds. Hyperspectral systems, while effective, are expensive research tools. Multispectral systems using a few wavelengths provide practically viable, less expensive and simpler imaging solutions. A multispectral system using three band-pass filters successfully detected and scored green barley kernels. Development of fast, accurate and low-cost multispectral systems is expected to have a profound effect on the acceptance of instrumental methods of grain grading by the grain industry. <s> BIB002 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Seed Purity and Variety Discrimination <s> Crop-to-crop transgene flow may affect the seed purity of non-transgenic rice varieties, resulting in unwanted biosafety consequences. The feasibility of a rapid and nondestructive determination of transgenic rice seeds from its non-transgenic counterparts was examined by using multispectral imaging system combined with chemometric data analysis. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA), least squares-support vector machines (LS-SVM), and PCA-back propagation neural network (PCA-BPNN) methods were applied to classify rice seeds according to their genetic origins. The results demonstrated that clear differences between non-transgenic and transgenic rice seeds could be easily visualized with the nondestructive determination method developed through this study and an excellent classification (up to 100% with LS-SVM model) can be achieved. It is concluded that multispectral imaging together with chemometric data analysis is a promising technique to identify transgenic rice seeds with high efficiency, providing bright prospects for future applications. <s> BIB003 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Seed Purity and Variety Discrimination <s> Author Summary: The feasibility of rapid and non-destructive classification of five different tomato seed cultivars was investigated by using visible and short-wave near infrared (Vis-NIR) spectra combined with chemometric approaches. Vis-NIR spectra containing 19 different wavelengths ranging from 375 nm to 970 nm were extracted from multispectral images of tomato seeds. Principal component analysis (PCA) was used for data exploration, while partial least squares discriminant analysis (PLS-DA) and support vector machine discriminant analysis (SVM-DA) were used to classify the five different tomato cultivars. The results showed very good classification accuracy for two independent test sets ranging from 94% to 100% for all tomato cultivars irrespective of chemometric methods. The overall classification error rates were 3.2% and 0.4% for the PLS-DA and SVM-DA calibration models, respectively. The results indicate that Vis-NIR spectra have the potential to be used for non-destructive discrimination of tomato seed cultivars with an opportunity to integrate them into plant genetic resource management, plant variety protection or registration programmes. <s> BIB004 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Seed Purity and Variety Discrimination <s> We present a method for multispectral seed phenotyping as a fast and robust tool for managing genebank accessions. A multispectral vision system was used to take images of the seeds of 20 diverse varieties of rice (approximately 30 seeds for each variety). This was followed by extraction of feature information from the images. Multivariate analysis of the feature data was used to classify seed phenotypes according to accession. The proportion of correctly classified rice seeds was 93%. We conclude that the multispectral image analysis could play a role in comparing incoming seeds against existing accessions, identifying different seed types within a sample of seeds and/or in checking whether regenerated seeds match the original seeds. <s> BIB005 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Seed Purity and Variety Discrimination <s> Soybean is an important oil- and protein-producing crop and over the last few decades soybean genetic transformation has made rapid strides. The probability of occurrence of transgene flow should be assessed, although the discrimination of conventional and transgenic soybean seeds and their hybrid descendants is difficult in fields. The feasibility of non-destructive discrimination of conventional and glyphosate-resistant soybean seeds and their hybrid descendants was examined by a multispectral imaging system combined with chemometric methods. Principal component analysis (PCA), partial least squares discriminant analysis (PLSDA), least squares-support vector machines (LS-SVM) and back propagation neural network (BPNN) methods were applied to classify soybean seeds. The current results demonstrated that clear differences among conventional and glyphosate-resistant soybean seeds and their hybrid descendants could be easily visualized and an excellent classification (98% with BPNN model) could be achieved. It was concluded that multispectral imaging together with chemometric methods would be a promising technique to identify transgenic soybean seeds with high efficiency. <s> BIB006 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Seed Purity and Variety Discrimination <s> Authentication of pasta is currently determined using molecular biology-based techniques focusing on DNA as the target analyte. Whilst proven to be effective, these approaches can be criticised as being destructive, time consuming, and requiring specialist instrument training. Advances in the field of multispectral imaging (MSI) and hyperspectral imaging (HSI) have facilitated the development of compact imaging platforms with the capability to rapidly differentiate a range of materials (inclusive of grains and seeds) based on surface colour, texture and chemical composition. This preliminary investigation evaluated the applicability of spectral imaging for identification and quantitation of durum wheat grain samples in relation to pasta authenticity. MSI and HSI were capable of rapidly distinguishing between durum wheat and adulterant common wheat cultivars and assigning percentage adulteration levels characterised by low biases and good repeatability estimates. The results demonstrated the potential for spectral imaging based seed/grain adulteration testing to augment existing standard molecular approaches for food authenticity testing. <s> BIB007
Discrimination among seed varieties and checking the seed purity plays a key great role in plant breeding programs, seed production, genebank management and in the general trading of seeds. Testing seed purity involves checking the presence of plant debris, foreign materials, weed seeds, contaminating species and broken and damaged seeds. Separating varieties and determining their critical properties in terms of distinctness, uniformity and stability standards are extremely significant for variety registration, intellectual property rights of plant breeders, as well as for developing new varieties in the markets  . Measurements of some essential morphological features are very vital in varietal identification and cultivars discrimination to highlight the phenotypic variations, but in some occasions these features may be not enough to act as discriminators among such cultivars. For instance, molecular markers and other biochemical methods are usually used for the identification and characterization of germplasm BIB004 . The traditional method currently used in international genebanks is usually done by visual inspection of a huge number of seed varieties to evaluate their phenotype features and to determine whether such varieties are completely new or have already been registered in the genebank. As these procedures are very time consuming and require highly skilled seed analysts, the development of a non-invasive, rapid and reliable technique for identifying and distinguishing the purity of verities is of boundless advantages  . Recent studies have demonstrated the ability of multispectral imaging in tandem with multivariate analyses for evaluating seed purity and varietal discrimination. The potential of using this technology in identifying and characterizing seed variety on the basis of differences in spectral signature and the ordinary morphological features was investigated in BIB005 BIB003    . By using illumination-based multispectral imaging system in the visible and near infrared region (375-970 nm) for varietal identification and discrimination tomato seeds, Shrestha et al.  examined eleven varieties either all together or in a pairwise manner with their offspring using their spectral fingerprints by the aid of multivariate analysis of normalized canonical discriminant analysis (nCDA), principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA). The nCDA model was used to minimize the distance within classes and to maximize the distance between classes. The developed models displayed overall classification accuracy of 100%, 85% and 82%, respectively. In reality, the nCDA method was employed as a supervised transformation building method to divide the image into regions of interest of different spectral signatures. When two different varieties have different spectral features, they will be appeared as two different region of interest in nCDA transformed image which will be easy to interpret and simple to segment for subsequent data analysis. When Olesen et al. BIB004 tried support vector machine discriminant analysis (SVM-DA) in discriminating tomato seeds of different cultivars, they achieved 100% discrimination accuracy. The same Shrestha et al. research group  used the same methodology in discriminating among tomato seeds of different maturity stages by following the same normalized canonical discriminant analysis (nCDA) algorithm. The data analysis showed a higher classification accuracy of tomato seed maturity measured by their germination potential. The result also suggests that seed maturity determination should be based on seeds instead of judging by fruit maturity or colour. With the same technique, Hansen et al. BIB005 collected both reflectance and fluorescence emission spectra by installing low-bass filters at 500, 600 and 700 nm in front of the CCD camera to discriminate twenty diverse varieties of rice. In total, each pixel contains 46 spectral values representing all possible chemical characteristics of the examined seeds. After segmenting multispectral imaging to locate rice seeds in a clear background, a set of 177 morphological and spectral features extracted from each single seed was modeled using canonical discriminant analysis (CDA) projections and k-nearest neighbor (k-NN) classifier to discriminate and enhance differences among rice seed varieties The results revealed that the proposed method was able to identify potential off-types within the seed lots of each accession with an accuracy of 93%., which may indeed be useful in managing genebank accessions by including only new accession and avoiding maintaining seeds that does not represent novel genetic diversity. For differentiation of wheat varieties and triticale varieties, Vrešak et al.  used a VideometerLab multispectral imaging system with 19 bands (375-950 nm) for acquiring multispectral images of seed samples and then extracted both morphological features (e.g., color, texture and major dimensions) as well as features from nCDA transformation images. Based on all extracted features, a k-nearest neighbor (k-NN) based model was built to discriminate different wheat varieties. The overall results on varieties separation using the proposed method revealed that Agostino variety was classified with accuracy of 97.4%and the overall classification accuracy for the rest of varieties was below 67%. They reported that, discrimination accuracy could be greatly improved up to 95.8% in case of grouping varieties together according to visual similarity. The feasibility of using LED illumination-based multispectral imaging (405-970 nm) associated with chemometric data analysis was examined by Liu et al. BIB003 for nondestructive discrimination of transgenic and non-transgenic rice seeds. Different chemometric data analysis methods such as principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), least squares-support vector machines (LS-SVM), and PCA-back propagation neural network (PCA-BPNN) were tested to see their performance in classifying rice seeds according to their genetic origins. The results revealed that LS-SVM model method exhibited the highest classification accuracy (100%) in distinguishing insect-resistant Bacillus thuringiensis transgenic rice seeds from non-transgenic ones. In another study, the same authors BIB006 followed the same routines in discrimination between conventional and glyphosate-resistant transgenic soybean seeds and their hybrid descendants by using the same multispectral system. Similarly, the results from this study attested the capability of the multispectral imaging supported by BPNN model in differentiation among conventional and glyphosate-resistant soybean seeds and their hybrid descendants with an excellent classification of 98%. In both studies, combining spectral data of the seeds with their corresponding morphological features extracted from their multispectral images (L*, a*, b* colour values, area, length, width and roundness) produced better predictive accuracies and helped in improving the discrimination for all examined multivariate analysis algorithms. Because the sorting process of seeds based on certain genetic features is a very time-consuming process that can take thousands of hours of labour to complete for a single average-sized breeding programmer, De La Fuente et al.  evaluated the ability of the VideometerLab3 multispectral imaging system to discriminate haploid from diploid maize seeds, testing the system on several maize genotypes that display varying difficulty in manual sorting. In addition to the 19 illuminated wavelength bands used for recording reflectance images, four band-pass filters at 400, 500, 600 and 700 nm were used for measuring fluorescence from the kernel surface producing a total of additional 60 excitation/emission combinations of fluorescence. The canonical discriminant analysis (CDA) method was used for segmenting the kernels form the background and to extract the main reflectance and fluorescence data either from the whole kernel or from embryo portions. The system has shown a great ability in discrimination between hybrids and haploids with accuracies ranging from 40% to 100% for different six genotypes. While hyperspectral imaging systems with relevant multivariate analyses are able to identify kernel damage, multispectral systems are much simpler and less expensive. In this regard, Shahin et al. BIB002 analyzed the hyperspectral data to select the most relevant wavelengths suitable for detecting green barely kernels. They then developed a multispectral imaging system with a single monochromatic camera and a high-speed filter wheel supported with three different band-pass filter centered at wavelengths of 580, 671 and 730 nm as nominated by the hyperspectral data analysis. The study indicated that the band ratio of 671/730 nm was very effective in scoring the greenness of barely kernels. Through experimentation, a threshold ratio of 671/730 = 75% helped in marking the transition to green kernels that related to the detection level by visual inspection; meaning that kernels having a band ratio less than 75% were considered green. Indeed, this is the first study that designed and developed a fully-functional multispectral imaging system using wavelengths resulting from the analysis of hyperspectral images of seed samples. In another study, Sumriddetchkajorn et al. BIB001 used a multispectral imaging system supported by simple image processing techniques for the identification of eight different breeds of rice. Under an ultraviolet excitation light at 265 nm (UVC), only two fluorescent wavelength bands at 540 nm and 575 nm selected by a liquid crystal tunable optical filter were used during image acquisition and simple image thresholding, blob filtering and blob analysis techniques were used for milled rice breed discrimination. The incident UVC light (at 265 nm) shining on milled rice grains was emitted with the corresponding fluorescent radiation and then received through the imaging lens. The tunable optical filter is electrically controlled in such a way that only the desired wavelength spectrum bands can get through to the 2-D digital camera with 45.1 ms switching time between them. The pixel fluorescence intensity at both images were then extracted for all rice breads and used as the basis of breed discrimination. The proposed method showed great potential in distinguishing rice breeds based on their amylose content. Furthermore, due to their action as germicidal light, UVC light used during rice breed identification can also kill unwanted germs attached to milled rice grains. For manufacturing pasta, the current EU law permits up to 3% (w/w) adventitious contamination of durum wheat (T. durum) with other common wheat (T. aestivum), and any malpractice during manufacturing by adding a higher percentage is considered a kind of adulteration that should be instantly prohibited. In a study conducted for seed authentication, Wilkes et al. BIB007 evaluated the applicability of multispectral imaging for the quantitation of durum wheat grain samples in relation to pasta authenticity by distinguishing between durum wheat T. durum and adulterant common wheat T. aestivum cultivars based on the spectral signature of each grain species. The performance of the system was evaluated on the basis of its ability to correctly assign the percentage of adulteration level (0, 0.5%, 2%, 3%, 5%, 10% and 100%) of the wheat test samples. Each grain was represented by thousands of pixels that can be scored based on their closeness to the spectral signature of T. aestivum or T. durum. The normalised-Canonical Discriminant Analysis model (nCDA) algorithm was then applied to calculate the likelihood of each grain being T. aestivum or T. durum based on composite spectral signature of the pixels within each grain. The resulting classification images, shown in Figure 4 , demonstrate close agreement with the assigned percentage of adulteration values. The results of this study indicated the ability of the spectral imaging in testing seed/grain adulteration. 