Investigating the Developmental and Phytochemical Characteristics of Aged Canola Seeds, and Providing a Practical Method for Detecting Age Seeds Using Hyperspectral Images

The aged seeds have a significant influence on seed vigor and corn growth. Therefore, it is vital for the planting industry to identify aged seeds. In this study, hyperspectral reflectance imaging (1,000–2,000 nm) was employed for identifying aged maize seeds using seeds harvested in different years. The average spectra of the embryo side, endosperm side, and both sides were extracted. The support vector machine (SVM) algorithm was used to develop classification models based on full spectra to evaluate the potential of hyperspectral imaging for maize seed detection and using the principal component analysis (PCA) and ANOVA to reduce data dimensionality and extract feature wavelengths. The classification models achieved perfect performance using full spectra with an accuracy of 100% for the prediction set. The performance of models established with the first three principal components was similar to full spectrum models, but that of PCA loading models was worse. Compared to other spectra, the two-band ratio (1,987 nm/1,079 nm) selected by ANOVA from embryo-side spectra achieved a better classification accuracy of 95% for the prediction set. The image texture features, including histogram statistics (HS) and gray-level co-occurrence matrix (GLCM), were extracted from the two-band ratio image to establish fusion models. The results demonstrated that the two-band ratio selected from embryo-side spectra combined with image texture features achieved the classification of maize seeds harvested in different years with an accuracy of 97.5% for the prediction set. The overall results indicated that combining the two wavelengths with image texture features could detect aged maize seeds effectively. The proposed method was conducive to the development of multi-spectral detection equipment.

Hyperspectral image (HSI) classification is one of the major problems in the field of remote sensing. Particularly, graph-based HSI classification is a promising topic and has received increasing attention in recent years. However, graphs with pixels as nodes generate large size graphs, thus increasing the computational burden. Moreover, satisfactory classification results are often not obtained without considering spatial information in constructing graph. To address these issues, this study proposes an efficient and effective semi-supervised spectral-spatial HSI classification method based on sparse superpixel graph (SSG). In the constructed sparse superpixels graph, each vertex represents a superpixel instead of a pixel, which greatly reduces the size of graph. Meanwhile, both spectral information and spatial structure are considered by using superpixel, local spatial connection and global spectral connection. To verify the effectiveness of the proposed method, three real hyperspectral images, Indian Pines, Pavia University and Salinas, are chosen to test the performance of our proposal. Experimental results show that the proposed method has good classification completion on the three benchmarks. Compared with several competitive superpixel-based HSI classification approaches, the method has the advantages of high classification accuracy (>97.85%) and rapid implementation (<10 s). This clearly favors the application of the proposed method in practice.

Detection of different stages of fungal infection in stored canola using near-infrared hyperspectral imaging

Challenges to deep sample imaging have necessitated the development of special techniques such as spatially offset optical spectroscopy to collect signals that have travelled through several layers of tissue. However, these techniques provide only spectral information in one dimension (i.e., depth). Here, we describe a general and practical method, referred to as Optical Recognition of Constructs Using Hyperspectral Imaging and Detection (ORCHID). The sensing strategy integrates (1) the spatial offset detection concept by computationally binning 2D optical data associated with digital offsets based on selected radial pixel distances from the excitation source; (2) hyperspectral imaging using tunable filter; and (3) digital image binding and collation. ORCHID is a versatile modality that is designed to collect optical signals deep inside samples across three spatial (X, Y, Z) as well as spectral dimensions. The ORCHID method is applicable to various optical techniques that exhibit narrow-band structures, from Raman scattering to quantum dot luminescence. Samples containing surface-enhanced Raman scattering (SERS)-active gold nanostar probes and quantum dots embedded in gel were used to show a proof of principle for the ORCHID concept. The resulting hyperspectral data cube is shown to spatially locate target emitting nanoparticle volumes and provide spectral information for in-depth 3D imaging.

Soluble solid content (SSC) is the most important attribute related to the quality and price of apples. The objective of this study was to detect the SSC of “Fuji” apple using hyperspectral imaging (HSI) combined with a deep learning algorithm. A novel, output-related deep learning algorithm, stacked weighted auto-encoders (SWAE) was proposed to extract output-related features based on pixel-level spectra of each sample. Relevant variables were identified and assigned to different weights by correlation analysis with the output variable. To quantitatively predict the SSC under the constraint of preferential reconstruction for quality-related variables, the deep features containing information about the SSC quality prediction were extracted, and then these features were input into gray wolf optimization-support vector regression (GWO-SVR) to quantitatively predict SSC. Furthermore, successive projection algorithm (SPA) and competitive adaptive reweighed sampling (CARS) were adopted as traditional feature selection algorithms to build GWO-SVR models for predicting the SSC in apples and comparing them with the deep learning models. The results showed that the proposed SWAE-GWO-SVR model yielded satisfactory performance with R p 2 of 0.9436, and RMSEP of 0.1328 °Brix, which demonstrate that deep learning combined with HSI can facilitate the nondestructive assessment of the internal quality attributes of fruits. Practical applications Traditional methods for detecting the soluble solid content (SSC) in apples are destructive and laborious. Therefore, the hyperspectral imaging (HSI) technique combined with deep learning was used to determine the SSC in apples in a rapid and nondestructive manner. The results indicated that combined method is feasible for SSC prediction. Thus, HSI combined with deep learning is considered as a promising method for detecting the SSC in apples.

The feasibility of using a visible/near-infrared hyperspectral imaging system with a wavelength range between 400 and 1000 nm to detect and differentiate different levels of aflatoxin B1 (AFB1 ) artificially titrated on maize kernel surface was examined. To reduce the color effects of maize kernels, image analysis was limited to a subset of original spectra (600 to 1000 nm). Residual staining from the AFB1 on the kernels surface was selected as regions of interest for analysis. Principal components analysis (PCA) was applied to reduce the dimensionality of hyperspectral image data, and then a stepwise factorial discriminant analysis (FDA) was performed on latent PCA variables. The results indicated that discriminant factors F2 can be used to separate control samples from all of the other groups of kernels with AFB1 inoculated, whereas the discriminant factors F1 can be used to identify maize kernels with levels of AFB1 as low as 10 ppb. An overall classification accuracy of 98% was achieved. Finally, the peaks of β coefficients of the discrimination factors F1 and F2 were analyzed and several key wavelengths identified for differentiating maize kernels with and without AFB1 , as well as those with differing levels of AFB1 inoculation. Results indicated that Vis/NIR hyperspectral imaging technology combined with the PCA-FDA was a practical method to detect and differentiate different levels of AFB1 artificially inoculated on the maize kernels surface. However, indicated the potential to detect and differentiate naturally occurring toxins in maize kernel.

The effect of light-emitting diodes (LED) with broad radiation spectra on developmental, physiological, and phytochemical characteristics of Greek sage (Salvia fruticosa L.) seedlings was assessed. Fluorescent (FL – control) tubes and four LED lights [AP67 (moderate blue, red and far-red), L20AP67 (moderate blue, red and far-red, high green), AP673L (moderate blue, high red) and NS1 (high blue and green, low red, high red : far-red, 1% ultraviolet)] were used in a growth chamber. Seedlings grown under FL, L20AP67 and AP673L exhibited the best morphological and developmental characteristics. FL led to inferior root biomass formation compared to all LEDs. AP67 promoted greater root-to-shoot dry weight ratio and dry-to-fresh overground and root weight ratios, but induced the least morphological and developmental characteristics. NS1 performed well regarding the root biomass production. Total phenolic content and the root growth capacity were not significantly affected. The present study demonstrates that L20AP67 and AP673L LEDs performed equally to FL light regarding the developmental characteristics. AP67 and NS1 may have the potential to be used for compact seedling production.

Phenotypes are a plant’s traits, such as its morphological and developmental observable characteristics, physiological traits, behaviors, and biochemical properties. Advances in imaging technology, analysis techniques, and in machine learning and statistical approaches can significantly advance the inquiry into phenotyping research. Image-based phenotyping is non-destructive, and therefore, allows for tracking of the dynamics of phenotypes during a plant’s life cycle. This chapter demonstrates and illustrates machine learning and statistical approaches, focusing on two-dimensional Red Green Blue and hyperspectral images, and three-dimensional computed tomography images. It illustrates different machine learning and statistical approaches used in plant phenotyping in the following order: unsupervised dimensional reduction approaches, supervised machine learning approaches for continuous traits, supervised machine learning approaches for binary traits, image segmentation approaches, and approaches combining multiple tracks of information or using temporary traits. To visualize the data in low-dimensional space and to predict traits from images, dimensional reduction approaches have been used.

El presente artículo muestra un método inicial, práctico y básico para la comparación de cámaras hiperespectrales. Una cámara hiperespectral permite tomar “cubos de imágenes” que constan de tres dimensiones, dos espaciales y una espectral en la que se almacena la reflexión de una escena a todos los espectros de onda entre el rango de la luz visible y el inicio del infrarrojo. Se utilizaron dos cámaras hiperespectrales para las pruebas, una marca Cubert modelo UHD-185 y una cámara diseñada por el Grupo Integrado de Ingeniería de la Universidad de la Coruña; la primera captura 127 espectros en el rango de los 450 a 998nm, la segunda toma hasta 1088 espectros, algunos repetidos, en el rango de 385 – 950nm. Se diseñó una interfaz para el control de la cámara no comercial, se realizaron capturas de forma simultánea a una escena bajo abundante luz solar y se comparó la información mediante Matlab al promediar y presentar de forma gráfica la información hiperespectral de un área común a ambas capturas. Entre los resultados más predominantes se encuentra el reconocimiento de la suavidad en la curva de datos de la cámara UHD-185 y la falta de picos pronunciados en momentos de caída de nivel de ciertos espectros; ambos criterios de selección importante según la aplicación. El procedimiento se puede aplicar para cámara de línea o de escena siempre y cuando las mismas retornen su información en una gama de imágenes que se puedan pasar a un formato .mat de Matlab.

The combination of hyperspectral imaging with multivariate data analysis methods has recently been applied to develop a nondestructive technique, required to determine the seed viability of artificially aged vegetable and cereal seeds. In this study, the potential of shortwave infrared hyperspectral imaging to determine the viability of naturally aged seeds was investigated and thereafter a model for online seed sorting system was developed. The hyperspectral images of 400 Hinoki cypress tree seeds were acquired, and germination tests were conducted for viability confirmation, which indicated 31.5% of the viable seeds. Partial least square discriminant analysis models with 179 variables in the wavelength region of 1000–1800 nm were developed with a maximum model accuracy of 98.4% and 93.8% in both the calibration and validation sets, respectively. The partial least square discriminant analysis beta coefficient revealed the key wavelengths to differentiate viable from nonviable seeds, determined based on the differences in the chemical compositions of the seeds, including their lipid and fatty acid contents, which may control the germination ability of the seeds. The most effective wavelengths were selected using two model-based variable selection methods (i.e., the variable importance of projection (15 variables) and the successive projections algorithm (8 variables)) to develop the model. The successive projections algorithm wavelength selection method was considered to develop a viability model, and its application to the raw data resulted in a prediction accuracy of 94.7% in the calibration set and 92.2% in the validation set. These results demonstrate the potential of shortwave infrared hyperspectral imaging spectroscopy as a powerful nondestructive method to determine the viability of Hinoki cypress seeds. This method could be applied to develop an online seed sorting system for seed companies and nurseries.

The vitality of corn seeds is a significant indicator for assessing the quality and yield of crops. In recent years, numerous information technologies have been adopted to analyze the seed vitality and provide support for efficient equipment. However, there are still some shortcomings in these technologies, which decrease the accuracy of identifying the seed vitality for various practical applications. In this paper, a synthesized classification method for seed vitality was proposed based on multisensor hyperspectral imaging. Firstly, hyperspectral images in the range of 370-1042 nm were collected for waxy corn seeds, which were subjected to aging processing with four periods of time (0, 3, 6, and 9 d). Besides, some preprocessing techniques including standard normal variate, multiplicative scatter correction, Savitzky-Golay smoothing, and first-order and second-order derivatives were employed to suppress noise interference in raw spectra. In addition, principal component analysis (PCA), 2nd derivatization, and successive projection algorithm (SPA) were adopted to select feature wavelengths. Moreover, SVM classification models based on full spectra and feature wavelengths were established. The results showed that, based on feature wavelengths selected by SPA, the SVM model preprocessed by multiplicative scatter correction (MSC) had the optimal performance. The training accuracy and testing accuracy of this model were 100% and 97.9167%, respectively. RMSE was 0.018 and R 2 was 0.875. Therefore, it can be demonstrated that the pattern recognition algorithm could achieve a high accuracy in classifying accelerated aging seeds. This algorithm provides a new method for machine learning (ML) in nondestructive detection of crops.

Cabbage (Brassica campestris L) is an important crop for Asian countries, and especially so for Korea, Japan and China. In order to achieve uniform and high-yield rates of cabbage product, seed lot quality needs to be controlled. Non-destructive evaluation of seed viability is an important technique for investigating seed quality. Hyperspectral imaging technique, which combines the features of imaging and spectroscopy, has been considered one of the most powerful nondestructive evaluation methods allowing comprehensive analysis of the physical and biochemical characteristics of materials. In this study, the feasibility of hyperspectral reflectance imaging technique was investigated for the evaluation of seed viability. For the investigation of viable and non-viable seeds, some viable seeds were artificially aged. Hyperspectral reflectance technique was used to discriminate aged cabbage seeds from normal seeds. The PLSDA and simple image threshold methods were applied to investigate the feasibility of distinguishing the aged seeds from the normal seeds. The discrimination accuracy was 96.7% for the calibration set and 96.9% for the test set. The resultant images from the PLS-DA method showed high classification performance in distinguishing the nonviable from the viable seeds, which is an impossible task by naked eye and by conventional color cameras. Hyperspectral reflectance imaging has good potential for discriminating nonviable cabbage seeds from massive amounts of viable cabbage seeds.

The sparsifying representation plays a significant role in compressive sensing (CS)-based hyperspectral (HS) imaging. Training the dictionaries for each dimension from HS samples is very beneficial to accurate reconstruction. However, the tensor dictionary learning algorithms are limited by a great amount of computation and convergence difficulties. We propose a least squares (LS) type multidimensional dictionary learning algorithm for CS-based HS imaging. We develop a practical method for the dictionary updating stage, which avoids the use of the Kronecker product and thus has lower computation complexity. To guarantee the convergence, we add a pruning stage to the algorithm to ensure the similarity and relativity among data in the spectral dimension. Our experimental results demonstrated that the dictionaries trained using the proposed algorithm performed better at CS-based HS image reconstruction than those trained with traditional LS-type dictionary learning algorithms and the commonly used analytical dictionaries.

Discriminant analysis of maize haploid seeds using near-infrared hyperspectral imaging integrated with multivariate methods

Prediction of TVB-N content in beef with packaging films using visible-near infrared hyperspectral imaging