Rapid and visual detection of the main chemical compositions in maize seeds based on Raman hyperspectral imaging

A pipeline for processing hyperspectral images, with a case of melanin-containing barley grains as an example

Analysis of hyperspectral images is of great interest in plant studies. Nowadays, this analysis is used more and more widely, so the development of hyperspectral image processing methods is an urgent task. This paper presents a hyperspectral image processing pipeline that includes: preprocessing, basic statistical analysis, visualization of a multichannel hyperspectral image, and solving classification and clustering problems using machine learning methods. The current version of the package implements the following methods: construction of a confidence interval of an arbitrary level for the difference of sample averages; verification of the similarity of intensity distributions of spectral lines for two sets of hyperspectral images on the basis of the Mann-Whitney U-criterion and Pearson's criterion of agreement; visualization in two-dimensional space using dimensionality reduction methods PCA, ISOMAP and UMAP; classification using linear or ridge regression, random forest and catboost; clustering of samples using the EM-algorithm. The software pipeline is implemented in Python using the Pandas, NumPy, OpenCV, SciPy, Sklearn, Umap, CatBoost and Plotly libraries. The source code is available at: https://github.com/igor2704/Hyperspectral_images. The pipeline was applied to identify melanin pigment in the shell of barley grains based on hyperspectral data. Visualization based on PCA, UMAP and ISOMAP methods, as well as the use of clustering algorithms, showed that a linear separation of grain samples with and without pigmentation could be performed with high accuracy based on hyperspectral data. The analysis revealed statistically significant differences in the distribution of median intensities for samples of images of grains with and without pigmentation. Thus, it was demonstrated that hyperspectral images can be used to determine the presence or absence of melanin in barley grains with great accuracy. The flexible and convenient tool created in this work will significantly increase the efficiency of hyperspectral image analysis.

Recent advances and applications of laser-based imaging techniques in food crops and products: a critical review

To meet the growing demand for food quality and safety, there is a pressing need for fast and visible techniques to monitor the food crop and product production processing, and to understand the chemical changes that occur during these processes. Herein, the fundamental principles, instruments, and characteristics of three major laser-based imaging techniques (LBITs), namely, laser-induced breakdown spectroscopy, Raman spectroscopy, and laser ablation-inductively coupled plasma-mass spectrometry, are introduced. Additionally, the advances, challenges, and prospects for the application of LBITs in food crops and products are discussed. In recent years, LBITs have played a crucial role in mapping primary metabolites, secondary metabolites, nanoparticles, toxic metals, and mineral elements in food crops, as well as visualizing food adulteration, composition changes, pesticide residue, microbial contamination, and elements in food products. However, LBITs are still facing challenges in achieving accurate and sensitive quantification of compositions due to the complex sample matrix and minimal laser sampling quantity. Thus, further research is required to develop comprehensive data processing strategies and signal enhancement methods. With the continued development of imaging methods and equipment, LBITs have the potential to further explore chemical distribution mechanisms and ensure the safety and quality of food crops and products.

Digital techniques and trends for seed phenotyping using optical sensors

BACKGROUND

The breeding of high-quality, high-yield, and disease-resistant varieties is closely related to food security. The investigation of breeding results relies on the evaluation of seed phenotype, which is a key step in the process of breeding. In the global digitalization trend, digital technology based on optical sensors can perform the digitization of seed phenotype in a non-contact, high throughput way, thus significantly improving breeding efficiency.

AIM OF REVIEW

This paper provides a comprehensive overview of the principles, characteristics, data processing methods, and bottlenecks associated with three digital technique types based on optical sensors: spectroscopy, digital imaging, and three-dimensional (3D) reconstruction techniques. In addition, the applicability and adaptability of digital techniques based on the optical sensors of maize seed phenotype traits, namely external visible phenotype (EVP) and internal invisible phenotype (IIP), are investigated. Furthermore, trends in future equipment, platform, phenotype data, and processing algorithms are discussed. This review offers conceptual and practical support for seed phenotype digitization based on optical sensors, which will provide reference and guidance for future research.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The digital techniques based on optical sensors can perform non-contact and high-throughput seed phenotype evaluation. Due to the distinct characteristics of optical sensors, matching suitable digital techniques according to seed phenotype traits can greatly reduce resource loss, and promote the efficiency of seed evaluation as well as breeding decision-making. Future research in phenotype equipment and platform, phenotype data, and processing algorithms will make digital techniques better meet the demands of seed phenotype evaluation, and promote automatic, integrated, and intelligent evaluation of seed phenotype, further helping to lessen the gap between digital techniques and seed phenotyping.

Protein and lipid content estimation in soybeans using Raman hyperspectral imaging

Unlike standard chemical analysis methods involving time-consuming, labor-intensive, and invasive pretreatment procedures, Raman hyperspectral imaging (HSI) can rapidly and non-destructively detect components without professional supervision. Generally, the Kjeldahl methods and Soxhlet extraction are used to chemically determine the protein and lipid content of soybeans. This study is aimed at developing a high-performance model for estimating soybean protein and lipid content using a non-destructive Raman HSI. Partial least squares regression (PLSR) techniques were used to develop the model using a calibration model based on 70% spectral data, and the remaining 30% of the data were used for validation. The results indicate that the Raman HSI, combined with PLSR, resulted in a protein and lipid model Rp2 of 0.90 and 0.82 with Root Mean Squared Error Prediction (RMSEP) 1.27 and 0.79, respectively. Additionally, this study successfully used the Raman HSI approach to create a prediction image showing the distribution of the targeted components, and could predict protein and lipid based on a single seeds.

Self-supervised maize kernel classification and segmentation for embryo identification

Introduction

Computer vision and deep learning (DL) techniques have succeeded in a wide range of diverse fields. Recently, these techniques have been successfully deployed in plant science applications to address food security, productivity, and environmental sustainability problems for a growing global population. However, training these DL models often necessitates the large-scale manual annotation of data which frequently becomes a tedious and time-and-resource- intensive process. Recent advances in self-supervised learning (SSL) methods have proven instrumental in overcoming these obstacles, using purely unlabeled datasets to pre-train DL models.

Methods

Here, we implement the popular self-supervised contrastive learning methods of NNCLR Nearest neighbor Contrastive Learning of visual Representations) and SimCLR (Simple framework for Contrastive Learning of visual Representations) for the classification of spatial orientation and segmentation of embryos of maize kernels. Maize kernels are imaged using a commercial high-throughput imaging system. This image data is often used in multiple downstream applications across both production and breeding applications, for instance, sorting for oil content based on segmenting and quantifying the scutellum's size and for classifying haploid and diploid kernels.

Results and discussion

We show that in both classification and segmentation problems, SSL techniques outperform their purely supervised transfer learning-based counterparts and are significantly more annotation efficient. Additionally, we show that a single SSL pre-trained model can be efficiently finetuned for both classification and segmentation, indicating good transferability across multiple downstream applications. Segmentation models with SSL-pretrained backbones produce DICE similarity coefficients of 0.81, higher than the 0.78 and 0.73 of those with ImageNet-pretrained and randomly initialized backbones, respectively. We observe that finetuning classification and segmentation models on as little as 1% annotation produces competitive results. These results show SSL provides a meaningful step forward in data efficiency with agricultural deep learning and computer vision.

Oil content analysis of corn seeds using a hand-held Raman spectrometer and spectral peak decomposition algorithm

Rapid, non-destructive and reliable detection of the oil content of corn seeds is important for development of high-oil corn. However, determination of the oil content is difficult using traditional methods for seed composition analysis. In this study, a hand-held Raman spectrometer was used with a spectral peak decomposition algorithm to determine the oil contents of corn seeds. Mature and waxy Zhengdan 958 corn seeds and mature Jingke 968 corn seeds were analyzed. Raman spectra were obtained in four regions of interest in the embryo of the seed. After analysis of the spectra, a characteristic spectral peak for the oil content was identified. A Gaussian curve fitting spectral peak decomposition algorithm was used to decompose the characteristic spectral peak of oil at 1657 cm^-1. This peak was used to determine the Raman spectral peak intensity for the oil content in the embryo and differences in the oil contents among seeds of varying maturity and different varieties. This method is feasible and effective for detection of corn seed oil.

Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning

Seed quality affects crop yield and the quality of agricultural products, and traditional identification methods are time-consuming, complex, and irreversibly destructive. This study aims to establish a fast, non-destructive, and effective approach for defect detection in maize seeds based on hyperspectral imaging (HSI) technology combined with deep learning. Raw spectra collected from maize seeds (200 each healthy and worm-eaten) were pre-processed using detrending (DE) and multiple scattering correction (MSC) to highlight the spectral differences between samples. A convolutional neural network architecture (CNN-FES) based on a feature selection mechanism was proposed according to the importance of wavelength in the target classification task. The results show that the subset of 24 feature wavelengths selected by the proposed CNN-FES can capture important feature information in the spectral data more effectively than the conventional successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) algorithms. In addition, a convolutional neural network architecture (CNN-ATM) based on an attentional classification mechanism was designed for one-dimensional spectral data classification and compared with three commonly used machine learning methods, linear discriminant analysis (LDA), random forest (RF), and support vector machine (SVM). The results show that the classification performance of the designed CNN-ATM on the full wavelength does not differ much from the above three methods, and the classification accuracy is above 90% on both the training and test sets. Meanwhile, the accuracy, sensitivity, and specificity of CNN-ATM based on feature wavelength modeling can reach up to 97.50%, 98.28%, and 96.77% at the highest, respectively. The study shows that hyperspectral imaging-based defect detection of maize seed is feasible and effective, and the proposed method has great potential for the processing and analysis of complex hyperspectral data.

The Influence of Biostimulants Used in Sustainable Agriculture for Antifungal Protection on the Chemical Composition of Winter Wheat Grain

Field studies were conducted from 2016 to 2019 (south-eastern Poland; 49°58'40.6″ N 22°33'11.3″ E) with the aim to identify the chemical composition of winter wheat grain upon foliar application of biostimulants, of which PlanTonic BIO (containing nettle and willow extracts) showed antifungal activity. The main chemical compositions and their spatial distribution in wheat grain were characterized by Raman spectroscopy technique. It was established that applied biostimulants and hydro-thermal conditions changed the chemical composition of the grain during all the studied years. A similar chemical composition of the grain was achieved in plants treated with synthetic preparations, including both intensive and extensive variants. The second group, in terms of an increase in fatty acid content, consists of grains of plants treated with biostimulants PlanTonic BIO, PlanTonic BIO + Natural Crop and PlanTonic BIO + Biofol Plex. The future of using biostimulants in crop production, including those containing salicylic acid and nettle extracts, appears to be a promising alternative to synthetic crop protection products.

Slight crack identification of cottonseed using air-coupled ultrasound with sound to image encoding

Slight crack of cottonseed is a critical factor influencing the germination rate of cotton due to foamed acid or water entering cottonseed through testa. However, it is very difficult to detect cottonseed with slight crack using common non-destructive detection methods, such as machine vision, optical spectroscopy, and thermal imaging, because slight crack has little effect on morphology, chemical substances or temperature. By contrast, the acoustic method shows a sensitivity to fine structure defects and demonstrates potential application in seed detection. This paper presents a novel method to detect slightly cracked cottonseed using air-coupled ultrasound with a light-weight vision transformer (ViT) and a sound-to-image encoding method. The echo signal of air-coupled ultrasound from cottonseed is obtained by non-contact and non-destructive methods. The intrinsic mode functions (IMFs) of ultrasound signal are obtained as the sound features using variational mode decomposition (VMD) approach. Then the sound features are converted into colorful images by a color encoding method. This method uses different colored lines to represent the changes of different values of IMFs according to the specified encoding period. A light-weight MobileViT method is utilized to identify the slightly cracked cottonseeds using encoding colorful images corresponding to cottonseeds. The experimental results show an average overall recognition accuracy of 90.7% for slightly cracked cottonseed from normal cottonseed, which indicates that the proposed method is reliable to applications in detection task of cottonseed with slight crack.

Variety classification of coated maize seeds based on Raman hyperspectral imaging

As an essential factor in quality assessment of maize seeds, variety purity profoundly impacts final yield and farmers' economic benefits. In this study, a novel method based on Raman hyperspectral imaging system was applied to achieve variety classification of coated maize seeds. A total of 760 maize seeds including 4 different varieties were evaluated. Raman spectral data of 400-1800 cm^-1 were extracted and preprocessed. Variable selection methods involved were modified competitive adaptive reweighted sampling (MCARS), successive projections algorithm (SPA), and their combination. In addition, MCARS was proposed for the first time in this paper as a stable search technology. The performance of support vector machine (SVM) models optimized by genetic algorithm (GA) was analyzed and compared with models based on random forest (RF) and back-propagation neural network (BPNN). Same models based on Vis-NIR spectral data were also established for comparison. Results showed that the MCARS-GA-SVM model based on Raman spectral data obtained the best performance with calibration accuracy of 99.29% and prediction accuracy of 100%, which were stable and easily replicated. In addition, the accuracy on the independent validation set was 96.88%, which proved that the model can be applied in practice. A more simplified MCARS-SPA-GA-SVM model, which contained only 3 variables, had more than 95% accuracy on each data set. This procedure can help to develop a real-time detection system to classify coated seed varieties with high accuracy, which is of great significance for assessing variety purity and increasing crop yield.

Integration of textural and spectral features of Raman hyperspectral imaging for quantitative determination of a single maize kernel mildew coupled with chemometrics

Maize mildew is a common phenomenon and it is essential to detect the mildew of a single maize kernel and prevent mildew from spreading around. In this study, a line-scanning Raman hyperspectral imaging system was applied to detect fungal spore quantity of a single maize kernel. Raman spectra were extracted while textural features were obtained to depict the maize mildew. Three kinds of modeling algorithms were used to establish the quantitative model to determine the fungal spore quantity of a single maize kernel. Then competitive adaptive reweighted sampling (CARS) was used to optimize characteristic variables. The optimal detection model was established with variables selected from the combination of Raman spectra and textural variance feature by PLSR. Results indicated that it was feasible to detect the fungal spore quantity of a single maize kernel by Raman hyperspectral technique. The study provided an in-situ and nondestructive alternative to detect fungal spore quantity.

Surface-Enhanced Raman Scattering Spectroscopy Combined With Chemical Imaging Analysis for Detecting Apple Valsa Canker at an Early Stage

Apple Valsa canker (AVC) with early incubation characteristics is a severe apple tree disease, resulting in significant orchards yield loss. Early detection of the infected trees is critical to prevent the disease from rapidly developing. Surface-enhanced Raman Scattering (SERS) spectroscopy with simplifies detection procedures and improves detection efficiency is a potential method for AVC detection. In this study, AVC early infected detection was proposed by combining SERS spectroscopy with the chemometrics methods and machine learning algorithms, and chemical distribution imaging was successfully applied to the analysis of disease dynamics. Results showed that the samples of healthy, early disease, and late disease sample datasets demonstrated significant clustering effects. The adaptive iterative reweighted penalized least squares (air-PLS) algorithm was used as the best baseline correction method to eliminate the interference of baseline shifts. The BP-ANN, ELM, Random Forest, and LS-SVM machine learning algorithms incorporating optimal spectral variables were utilized to establish discriminative models to detect of the AVC disease stage. The accuracy of these models was above 90%. SERS chemical imaging results showed that cellulose and lignin were significantly reduced at the phloem disease-health junction under AVC stress. These results suggested that SERS spectroscopy combined with chemical imaging analysis for early detection of the AVC disease was feasible and promising. This study provided a practical method for the rapidly diagnosing of apple orchard diseases.

Magneto-Primed Triticale Seeds Studied by Micro-Raman Spectra

The spectroscopy technique of Micro-Raman is an appropriate method to investigate the microscopic structure of internally heterogeneous (i.e., composed of multiple layers) agro-food products. The effects of applying magnetic fields (magneto-priming technique) and imbibition on the chemical makeup of Triticale seed were studied, particularly in its pericarp, germ and endosperm parts, with the help of Micro-Raman. In light of the results obtained, the magneto-primed seeds soaked in water presented a greater number of chemical compounds than the control seeds, although those treatments were not as effective as the ones with only magneto-priming. The effects of the magneto-priming treatment were especially noticeable in the endosperm due to the large number of chemical compounds identified. The seed composition differences among treatments showed that the use of Micro-Raman jointly with magneto-priming is an appropriate method to obtain and analyse information of the key components of Triticale seeds, notably regarding their pericarp and endosperm.

Rapid detection of seven indexes in sheep serum based on Raman spectroscopy combined with DOSC-SPA-PLSR-DS model

Hepatic fascioliasis, ketosis of pregnancy, toxemia of pregnancy and other common sheep diseases will directly affect the concentration (/enzymatic activity) of seven indicators, such as cortisol and high-density lipoprotein cholesterol (HDL-C) in sheep serum. Whether the concentrations (/enzymatic activity) of these indicators can be detected quickly will directly affect the prevention of sheep diseases and the targeted adjustment of breeding methods, thereby affecting the economic benefits of sheep breeding. In this research, we established partial least square regression (PLSR), support vector regression based on genetic algorithm optimization (GA-SVR) and extreme learning machine (ELM) models. Due to the large differences in the content of different substances, it is difficult to directly use the RMSE to evaluate the quantitative effect of the model. This study is the first to propose conducting deviation standardization (DS) for the determination results of various substances. To further improve the performance of the model, we use the successive projections algorithm (SPA) to optimize feature extraction and combine it with the better-performing PLSR model for training. The results show that the optimized DOSC-SPA-PLSR-DS quantitative model has better determination results for 101 sheep serum samples. The average RMSE_p* of the concentration of the six substances decreased from 0.0408 to 0.0387, the R_p² increased from 0.9758 to 0.9846, and the running time was reduced from 0.1659 to 0.0008 s. And the determination performance of lipase (LPS) enzymatic activity has also been improved. The results of this research show that sheep serum Raman spectroscopy combined with DOSC-SPA-PLSR-DS optimization can efficiently monitor the concentration (/enzyme activity) of seven indicators in real time and provide a new strategy for future intelligent supervision of animal husbandry.

Quantitative visualization of photosynthetic pigments in tea leaves based on Raman spectroscopy and calibration model transfer

BACKGROUND

Photosynthetic pigments participating in the absorption, transformation and transfer of light energy play a very important role in plant growth. While, the spatial distribution of foliar pigments is an important indicator of environmental stress, such as pests, diseases and heavy metal stress.

RESULTS

In this paper, in situ quantitative visualization of chlorophyll and carotenoid was realized by combining the Raman spectroscopy with calibration model transfer, and a laboratory Raman spectral model was successfully extended to a portable field spectral measurement. Firstly, a nondestructive and fast model for determination of chlorophyll and carotenoid in tea leaf was established based on confocal micro-Raman spectrometer in the laboratory. Then the spectral model was extended to a real-time foliar map scanning spectra of a field portable Raman spectrometer through calibration model transfer, and the spectral variation between the confocal micro-Raman spectrometer in the laboratory and the portable Raman spectrometer were effectively corrected by the direct standardization (DS) algorithm. The portable map scanning Raman spectra of the tea leaves after the model transfer were got into the established quantitative determination model to predict the concentration of photosynthetic pigments at each pixel of the tea leaves. The predicted photosynthetic pigments concentration of each pixel was imaged to illustrate the distribution map of foliar pigments. Statistical analysis showed that the predicted pigment contents were highly correlated with the real contents.

CONCLUSIONS

It can be concluded that the Raman spectroscopy was applicable for in situ, non-destructive and rapid quantitative detecting and imaging of photosynthetic pigment concentration in tea leaves, and the spectral detection model established based on the laboratory Raman spectrometer can be applied to a portable field spectrometer for quantitatively imaging of the foliar pigments.

Automation and Scale Up of Somatic Embryogenesis for Commercial Plant Production, With Emphasis on Conifers

For large scale production of clonal plants, somatic embryogenesis (SE) has many advantages over other clonal propagation methods such as the rooting of cuttings. In particular, the SE process is more suited to scale up and automation, thereby reducing labor costs and increasing the reliability of the production process. Furthermore, the plants resulting from SE closely resemble those from seeds, as somatic embryos, like zygotic (seed) embryos, develop with good connection between root and shoot, and without the plagiotropism often associated with propagation by cuttings. For practical purposes in breeding programs and for deployment of elite clones, it is valuable that a virtually unlimited number of SE plants can be generated from one original seed embryo; and SE cultures (clones) can be cryostored for at least 20 years, allowing long-term testing of clones. To date, there has however been limited use of SE for large-scale plant production mainly because without automation it is labor-intensive. Development of automation is particularly attractive in countries with high labor costs, where conifer forestry is often of great economic importance. Various approaches for automating SE processes are under investigation and the progress is reviewed here, with emphasis on conifers. These approaches include simplification of culture routines with preference for liquid rather than solid cultures, use of robotics and automation for the harvest of selected individual mature embryos, followed by automated handling of germination and subsequent planting. Different approaches to handle the processes of somatic embryogenesis in conifers are outlined below, followed by an update on efforts to automate the different steps, which are nearing an operational stage.