Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Prediction of Seed Structure <s> A simple imaging system has been developed for acquiring multivariate images in order to characterise the heterogeneity of food materials. The objective of the present work is, first, to demonstrate the capability of this acquisition system to discriminate food products of different natures. Secondly, our goal is to apply Partial Least Squares regression on these multivariate images and to evaluate the interest of various strategies of classification. A data set containing 24 images (702 X 524) acquired at different wavelengths for four food products is analysed. After the establishment of the PLS2 models employed for predicting the indicator variables, four strategies of classification of observations are tested. The first classification is done by selecting the largest component of the indicator variables. The others are based on the measurement of distances to the barycentres of the qualitative groups. Distances calculated can be either Euclidian distances or Mahalonobis distances. Except the strategy based on the Euclidian distance on scores, the strategies are rather equivalent, with a slight advantage to the Euclidian distance on predicted indicators. Another possibility addressed by the use of linear discriminant analysis (LDA) on multivariate images is to represent the qualitative groups as artificial images. The largest confusion appears between both cereal products while others are well classified. <s> BIB001 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Prediction of Seed Structure <s> Variations in the quality of wheat kernels can be an important problem in the cereal industry. In particular, desiccation conditions play an essential role in both the technological characteristics of the kernel and its ability to sprout. In planta desiccation constitutes a key stage in the determinism of the functional properties of seeds. The impact of desiccation on the endosperm texture of seed is presented in this work. A simple imaging system had previously been developed to acquire multivariate images to characterize the heterogeneity of food materials. A special algorithm for the use under principal component analysis (PCA) was developed to process the acquired multivariate images. Wheat grains were collected at physiological maturity, and were subjected to two types of drying conditions that induced different kinetics of water loss. A data set containing 24 images (dimensioned 702 × 524 pixels) corresponding to the different desiccation stages of wheat kernels was acquired at different wavelengths and then analyzed. A comparison of the images of kernel sections highlighted changes in kernel texture as a function of their drying conditions. Slow drying led to a floury texture, whereas fast drying caused a glassy texture. The automated imaging system thus developed is sufficiently rapid and economical to enable the characterization in large collections of grain texture as a function of time and water content. <s> BIB002 </s> Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview <s> Prediction of Seed Structure <s> In the durum wheat industry, the milling process leads to the production of different fractions such as semolina, flours, bran and thirds. The histological origin and composition of these fractions are not easy to determine. Moreover, the proportions of kernel tissue in a given fraction depend on the wheat cultivar. In the present work, 4 batches of durum wheat were processed in an experimental semolina pilot plant leading to the extraction of 18 representative fractions. Multispectral images of both the final fractions and wheat kernels have been acquired with an in-house imaging system. By performing principal component analysis and a Kmeans procedure on the multispectral images of each fraction, a reference spectral fingerprint was assessed for every fraction. For each pixel of the images of kernel cross-section, it was possible to calculate its probability of belonging to a typical group of fingerprint, and then to build up a probability image. These probability images, presented in false colors, emphasized the areas in the kernels from which a given milled fraction was extracted. The images obtained in this way enable a more precise identification of the histological origin of the different milled fractions. <s> BIB003
Detecting some key features in seeds such as desiccation represents another challenge in checking the purity of seeds and its acceptability and shelf life because it affects viability and germination ability. In this regard, Jaillais et al. BIB002 utilized an illumination-based multispectral imaging system supported with eight LEDs operating in the 360-950 nm range (360, 370, 400, 470, 524, 626, 875 and 950 nm) to investigate the effects of desiccation on the texture of wheat endosperm and detecting different desiccation regimes (fast and slow drying). They developed a specific software program to define and control the integration time of the camera and the selection of the LEDs and the photon integration times were adjusted experimentally to prevent any under-or over-exposition. A colour image was acquired at each single LED yielding a total of 3 × 8 = 24 image slices for each single seed. The spectral data extracted from the seeds was explored by PCA to get an overview of the systematic spectral variation in the spectral data and to explore the possibility of grouping the seeds of similar spectral profiles. The results and multispectral image analyses revealed that the change in endosperm texture could be observed directly on the color image obtained with illumination at 360 nm. Under slow-drying conditions, the endosperm texture of seeds remained floury from the start to the end of desiccation treatment; meanwhile, the endosperm texture changed and became glassy under a fast desiccation process. The same Jaillais et al. research team BIB003 used the same multispectral imaging system configuration for acquiring reflectance images of both final milled wheat fractions and wheat  Detecting some key features in seeds such as desiccation represents another challenge in checking the purity of seeds and its acceptability and shelf life because it affects viability and germination ability. In this regard, Jaillais et al. BIB002 utilized an illumination-based multispectral imaging system supported with eight LEDs operating in the 360-950 nm range (360, 370, 400, 470, 524, 626, 875 and 950 nm) to investigate the effects of desiccation on the texture of wheat endosperm and detecting different desiccation regimes (fast and slow drying). They developed a specific software program to define and control the integration time of the camera and the selection of the LEDs and the photon integration times were adjusted experimentally to prevent any under-or over-exposition. A colour image was acquired at each single LED yielding a total of 3 × 8 = 24 image slices for each single seed. The spectral data extracted from the seeds was explored by PCA to get an overview of the systematic spectral variation in the spectral data and to explore the possibility of grouping the seeds of similar spectral profiles. The results and multispectral image analyses revealed that the change in endosperm texture could be observed directly on the color image obtained with illumination at 360 nm. Under slow-drying conditions, the endosperm texture of seeds remained floury from the start to the end of desiccation treatment; meanwhile, the endosperm texture changed and became glassy under a fast desiccation process. The same Jaillais et al. research team BIB003 used the same multispectral imaging system configuration for acquiring reflectance images of both final milled wheat fractions and wheat kernels to identify histological origin of the mills in the kernels. In other words, the main objective of this study was to determine if the spectral characteristics of the milled products can be exploited for relevantly labeling the histological regions in images of wheat kernels. The multispectral images (of 24 sub-image slices each) were processed to create pseudocolour color images to show the proportions of each milled product extracted from the kernel tissues by computing probability images from the multispectral images of kernel sections. The intensities in such images indicate the localization from which a given milled product was extracted. For example, the probability image of coarse bran (CB) shows that this milled product was mainly extracted from the out-layer part of the grain where the intense red color was mainly located at the periphery of the kernel. Thence, the purest semolina (SE) appeared to be extracted from the endosperm region and the break flours (BF) had less clearly identified spectral signature to be extracted from a specific location ( Figure 5 ). purest semolina (SE) appeared to be extracted from the endosperm region and the break flours (BF) had less clearly identified spectral signature to be extracted from a specific location ( Figure 5 ). Chevallier et al. BIB001 employed a similar multispectral imaging system to investigate the potential of such a system to characterize the heterogeneity of food materials (maize, pea, soya bean meal, wheat). Spectral data of the samples were extracted and then analyzed by partial least squares-discriminant analysis (PLS-DA). When the developed model was applied to the original multispectral images, a good visualization was achieved and the proportion of correctly identified pixels observed on the validation set was 75%, 83%, 98%, and 89% for maize, pea, soya bean meal, and wheat, respectively A fully mechanized LED-pulsed multispectral system was designed and developed by Pearson et al.  using three visible and three near-infrared LEDs with peak emission wavelengths at 470, 527, 624, 850, 940 and 1070 nm for sorting wheat kernels. The multispectral data of wheat kernels were rapidly collected by operating one LED at a time and consequently recording the reflected light from wheat kernels as they dropped off a 20-cm long feeder chute. Sorting of the kernels was conducted by pneumatic system in which an air valve diverted the wheat kernels to the classification assignment. The system achieved high performance in three different applications: (i) separating white and red wheat kernels, (ii) separating undamaged wheat kernels from those ones damaged by Fusarium head blight, and (iii) separating wheat kernels based on their average protein content to three classes i.e. high, medium, and low. By recording sequences of images by changing the spectral conditions, Novales and Bertrand [101] acquired 12 multispectral images at different combinations of excitation and emission filters to identify kernel tissues based on their natural fluorescence characteristics. They used three excitation filters 360, 400 and 510 nm and four emission (fluorescence) filters at 436, 486, 508 and 546 nm by using two filter wheels to select the excitation and emission wavelengths. The most discriminant images among the combined 12 acquired fluorescence images were selected by stepwise discriminant analysis for the identification of kernel tissues. Images were labeled according to the nature of the tissues by attributing each pixel in the sequence of images a qualitative group number corresponding to each tissue. The resulting labeled images exhibit visualized information about spatial distribution of the seed compositional components. As each tissue has a distinct fluorescence Chevallier et al. BIB001 employed a similar multispectral imaging system to investigate the potential of such a system to characterize the heterogeneity of food materials (maize, pea, soya bean meal, wheat). Spectral data of the samples were extracted and then analyzed by partial least squares-discriminant analysis (PLS-DA). When the developed model was applied to the original multispectral images, a good visualization was achieved and the proportion of correctly identified pixels observed on the validation set was 75%, 83%, 98%, and 89% for maize, pea, soya bean meal, and wheat, respectively. A fully mechanized LED-pulsed multispectral system was designed and developed by Pearson et al.  using three visible and three near-infrared LEDs with peak emission wavelengths at 470, 527, 624, 850, 940 and 1070 nm for sorting wheat kernels. The multispectral data of wheat kernels were rapidly collected by operating one LED at a time and consequently recording the reflected light from wheat kernels as they dropped off a 20-cm long feeder chute. Sorting of the kernels was conducted by pneumatic system in which an air valve diverted the wheat kernels to the classification assignment. The system achieved high performance in three different applications: (i) separating white and red wheat kernels, (ii) separating undamaged wheat kernels from those ones damaged by Fusarium head blight, and (iii) separating wheat kernels based on their average protein content to three classes i.e., high, medium, and low. By recording sequences of images by changing the spectral conditions, Novales and Bertrand [101] acquired 12 multispectral images at different combinations of excitation and emission filters to identify kernel tissues based on their natural fluorescence characteristics. They used three excitation filters 360, 400 and 510 nm and four emission (fluorescence) filters at 436, 486, 508 and 546 nm by using two filter wheels to select the excitation and emission wavelengths. The most discriminant images among the combined 12 acquired fluorescence images were selected by stepwise discriminant analysis for the identification of kernel tissues. Images were labeled according to the nature of the tissues by attributing each pixel in the sequence of images a qualitative group number corresponding to each tissue. The resulting labeled images exhibit visualized information about spatial distribution of the seed compositional components. As each tissue has a distinct fluorescence behavior, the results revealed that five main tissues (horny endosperm, floury endosperm, germ, coats and tip cap) can be distinguished with more than 98.8 % of the pixels being correctly identified.