Non-destructive analysis of germination percentage, germination energy and simple vigour index on wheat seeds during storage by Vis/NIR and SWIR hyperspectral imaging

Green analytical assay for the viability assessment of single maize seeds using double-threshold strategy for catalase activity and malondialdehyde content

The development of nondestructive technology for the detection of seed viability is challenging. In this study, to establish a green and effective method for the viability assessment of single maize seeds, a two-stage seed viability detection method was proposed. The catalase (CAT) activity and malondialdehyde (MDA) content were selected as the most key biochemical components affecting maize seed viability, and regression prediction models were developed based on their hyperspectral information and a data fusion strategy. Qualitative discrimination models for seed viability evaluation were constructed based on the predicted response values of the selected key biochemical components. The results showed that the double components thresholds strategy achieved the highest discrimination accuracy (92.9%), providing a crucial approach for the rapid and environmentally friendly detection of seed viability.

A chemometric approach to assess the oil composition and content of microwave-treated mustard (Brassica juncea) seeds using Vis-NIR-SWIR hyperspectral imaging

The wide gap between the demand and supply of edible mustard oil can be overcome to a certain extent by enhancing the oil-recovery during mechanical oil expression. It has been reported that microwave (MW) pre-treatment of mustard seeds can have a positive effect on the availability of mechanically expressible oil. Hyperspectral imaging (HSI) was used to understand the change in spatial spread of oil in the microwave (MW) treated seeds with bed thickness and time of exposure as variables, using visible near-infrared (Vis-NIR, 400-1000 nm) and short-wave infrared (SWIR, 1000-1700 nm) systems. The spectral data was analysed using chemometric techniques such as partial least square discriminant analysis (PLS-DA) and regression (PLSR) to develop prediction models. The PLS-DA model demonstrated a strong capability to classify the mustard seeds subjected to different MW pre-treatments from control samples with a high accuracy level of 96.6 and 99.5% for Vis-NIR and SWIR-HSI, respectively. PLSR model developed with SWIR-HSI spectral data predicted (R2 > 0.90) the oil content and fatty acid components such as oleic acid, erucic acid, saturated fatty acids, and PUFAs closest to the results obtained by analytical techniques. However, these predictions (R2 > 0.70) were less accurate while using the Vis-NIR spectral data.

Non-destructive identification of Pseudostellaria heterophylla from different geographical origins by Vis/NIR and SWIR hyperspectral imaging techniques

The composition of Pseudostellaria heterophylla (Tai-Zi-Shen, TZS) is greatly influenced by the growing area of the plants, making it significant to distinguish the origins of TZS. However, traditional methods for TZS origin identification are time-consuming, laborious, and destructive. To address this, two or three TZS accessions were selected from four different regions of China, with each of these resources including distinct quality grades of TZS samples. The visible near-infrared (Vis/NIR) and short-wave infrared (SWIR) hyperspectral information from these samples were then collected. Fast and high-precision methods to identify the origins of TZS were developed by combining various preprocessing algorithms, feature band extraction algorithms (CARS and SPA), traditional two-stage machine learning classifiers (PLS-DA, SVM, and RF), and an end-to-end deep learning classifier (DCNN). Specifically, SWIR hyperspectral information outperformed Vis/NIR hyperspectral information in detecting geographic origins of TZS. The SPA algorithm proved particularly effective in extracting SWIR information that was highly correlated with the origins of TZS. The corresponding FD-SPA-SVM model reduced the number of bands by 77.2% and improved the model accuracy from 97.6% to 98.1% compared to the full-band FD-SVM model. Overall, two sets of fast and high-precision models, SWIR-FD-SPA-SVM and SWIR-FD-DCNN, were established, achieving accuracies of 98.1% and 98.7% respectively. This work provides a potentially efficient alternative for rapidly detecting the origins of TZS during actual production.

An end-to-end seed vigor prediction model for imbalanced samples using hyperspectral image

Hyperspectral imaging is a key technology for non-destructive detection of seed vigor presently due to its capability to capture variations of optical properties in seeds. As the seed vigor data depends on the actual germination rate, it inevitably results in an imbalance between positive and negative samples. Additionally, hyperspectral image (HSI) suffers from feature redundancy and collinearity due to its inclusion of hundreds of wavelengths. It also creates a challenge to extract effective wavelength information in feature selection, however, which limits the ability of deep learning to extract features from HSI and accurately predict seed vigor. Accordingly, in this paper, we proposed a Focal-WAResNet network to predict seed vigor end-to-end, which improves the network performance and feature representation capability, and improves the accuracy of seed vigor prediction. Firstly, the focal loss function is utilized to adjust the loss weights of different sample categories to solve the problem of sample imbalance. Secondly, a WAResNet network is proposed to select characteristic wavelengths and predict seed vigor end-to-end, focusing on wavelengths with higher network weights, which enhance the ability of seed vigor prediction. To validate the effectiveness of this method, this study collected HSI of maize seeds for experimental verification, providing a reference for plant breeding. The experimental results demonstrate a significant improvement in classification performance compared to other state-of-the-art methods, with an accuracy up to 98.48% and an F1 score of 95.9%.

Non-destructive detection of single-seed viability in maize using hyperspectral imaging technology and multi-scale 3D convolutional neural network

The viability of Zea mays seed plays a critical role in determining the yield of corn. Therefore, developing a fast and non-destructive method is essential for rapid and large-scale seed viability detection and is of great significance for agriculture, breeding, and germplasm preservation. In this study, hyperspectral imaging (HSI) technology was used to obtain images and spectral information of maize seeds with different aging stages. To reduce data input and improve model detection speed while obtaining more stable prediction results, successive projections algorithm (SPA) was used to extract key wavelengths that characterize seed viability, then key wavelength images of maize seed were divided into small blocks with 5 pixels ×5 pixels and fed into a multi-scale 3D convolutional neural network (3DCNN) for further optimizing the discrimination possibility of single-seed viability. The final discriminant result of single-seed viability was determined by comprehensively evaluating the result of all small blocks belonging to the same seed with the voting algorithm. The results showed that the multi-scale 3DCNN model achieved an accuracy of 90.67% for the discrimination of single-seed viability on the test set. Furthermore, an effort to reduce labor and avoid the misclassification caused by human subjective factors, a YOLOv7 model and a Mask R-CNN model were constructed respectively for germination judgment and bud length detection in this study, the result showed that mean average precision (mAP) of YOLOv7 model could reach 99.7%, and the determination coefficient of Mask R-CNN model was 0.98. Overall, this study provided a feasible solution for detecting maize seed viability using HSI technology and multi-scale 3DCNN, which was crucial for large-scale screening of viable seeds. This study provided theoretical support for improving planting quality and crop yield.

Response mechanism and rapid detection of phenotypic information in rice root under heavy metal stress

The root is an important organ affecting cadmium accumulation in grains, but there is no comprehensive research involving rice root phenotype under cadmium stress yet. To assess the effect of cadmium on root phenotypes, this paper investigated the response mechanism of phenotypic information including cadmium accumulation, adversity physiology, morphological parameters, and microstructure characteristics, and explored rapid detection methods of cadmium accumulation and adversity physiology. We found that cadmium had the effect of "low-promotion and high-inhibition" on root phenotypes. In addition, the rapid detection of cadmium (Cd), soluble protein (SP), and malondialdehyde (MDA) were achieved based on spectroscopic technology and chemometrics, where the optimal prediction model was least squares support vector machine (LS-SVM) based on the full spectrum (R_p=0.9958) for Cd, competitive adaptive reweighted sampling-extreme learning machine (CARS-ELM) (R_p=0.9161) for SP and CARS-ELM (R_p=0.9021) for MDA, all with R_p higher than 0.9. Surprisingly, it took only about 3 min, which was more than 90% reduction in detection time compared with laboratory analysis, demonstrating the excellent ability of spectroscopy for root phenotype detection. These results reveal response mechanism to heavy metal and provide rapid detection method for phenotypic information, which can substantially contribute to crop heavy metal control and food safety supervision.

Detection of peanut seed vigor based on hyperspectral imaging and chemometrics

Rapid nondestructive testing of peanut seed vigor is of great significance in current research. Before seeds are sown, effective screening of high-quality seeds for planting is crucial to improve the quality of crop yield, and seed vitality is one of the important indicators to evaluate seed quality, which can represent the potential ability of seeds to germinate quickly and whole and grow into normal seedlings or plants. Meanwhile, the advantage of nondestructive testing technology is that the seeds themselves will not be damaged. In this study, hyperspectral technology and superoxide dismutase activity were used to detect peanut seed vigor. To investigate peanut seed vigor and predict superoxide dismutase activity, spectral characteristics of peanut seeds in the wavelength range of 400-1000 nm were analyzed. The spectral data are processed by a variety of hot spot algorithms. Spectral data were preprocessed with Savitzky-Golay (SG), multivariate scatter correction (MSC), and median filtering (MF), which can effectively to reduce the effects of baseline drift and tilt. CatBoost and Gradient Boosted Decision Tree were used for feature band extraction, the top five weights of the characteristic bands of peanut seed vigor classification are 425.48nm, 930.8nm, 965.32nm, 984.0nm, and 994.7nm. XGBoost, LightGBM, Support Vector Machine and Random Forest were used for modeling of seed vitality classification. XGBoost and partial least squares regression were used to establish superoxide dismutase activity value regression model. The results indicated that MF-CatBoost-LightGBM was the best model for peanut seed vigor classification, and the accuracy result was 90.83%. MSC-CatBoost-PLSR was the optimal regression model of superoxide dismutase activity value. The results show that the R² was 0.9787 and the RMSE value was 0.0566. The results suggested that hyperspectral technology could correlate the external manifestation of effective peanut seed vigor.

Hyperspectral imaging with machine learning for non-destructive classification of Astragalus membranaceus var. mongholicus, Astragalus membranaceus, and similar seeds

The roots of Astragalus membranaceus var. mongholicus (AMM) and A. membranaceus (AM) are widely used in traditional Chinese medicine. Although AMM has higher yields and accounts for a larger market share, its cultivation is fraught with challenges, including mixed germplasm resources and widespread adulteration of commercial seeds. Current methods for distinguishing Astragalus seeds from similar (SM) seeds are time-consuming, laborious, and destructive. To establish a non-destructive method, AMM, AM, and SM seeds were collected from various production areas. Machine vision and hyperspectral imaging (HSI) were used to collect morphological data and spectral data of each seed batch, which was used to establish discriminant models through various algorithms. Several preprocessing methods based on hyperspectral data were compared, including multiplicative scatter correction (MSC), standard normal variable (SNV), and first derivative (FD). Then selection methods for identifying informative features in the above data were compared, including successive projections algorithm (SPA), uninformative variable elimination (UVE), and competitive adaptive reweighted sampling (CARS). The results showed that support vector machine (SVM) modeling of machine vision data could distinguish Astragalus seeds from SM with >99% accuracy, but could not satisfactorily distinguish AMM seeds from AM. The FD-UVE-SVM model based on hyperspectral data reached 100.0% accuracy in the validation set. Another 90 seeds were tested, and the recognition accuracy was 100.0%, supporting the stability of the model. In summary, HSI data can be applied to discriminate among the seeds of AMM, AM, and SM non-destructively and with high accuracy, which can drive standardization in the Astragalus production industry.

Silicon Supplementation Alleviates Adverse Effects of Ammonium on Ssamchoo Grown in Home Cultivation System

Ssamchoo is recently attracting attention as a household hydroponic vegetable in Korea. It has a refreshing texture and a rich content of vitamins and fiber. Ssamchoo with a wide leaf area is suitable for traditional ssam or vegetable wraps, as well as a vegetable for salads; thus, it can be used in a variety of dishes. However, Ssamchoo plants responds sensitively to the nutrient solution, and it is often difficult to secure sufficient leaf area and robust growth using a commercial nutrient solution for leafy vegetables. This study consisted of three experiments conducted to develop the nutrient solution for Ssamchoo grown in a newly developed home hydroponic cultivation system using light-emitting diodes as the sole source of light. In the first experiment, growth and development of Ssamchoo in a representative commercial nutrient solution, Peters Professional (20-20-20, The Scotts Co., Marysville, OH, USA), was compared with laboratory-prepared nutrient solutions, GNU1 and GNU2. As a result, the Ssamchoo grown in Peters Professional had a high NH₄⁺ content in the tissue, leaf yellowing, darkened root color, and suppressed root hair development. In addition, adverse effects of ammonium such as low fresh weight and shorter shoot length were observed. In the second experiment, Peters Professional was excluded, and the ratio of NO₃^- to NH₄⁺ in the GNU1 and GNU2 nutrient solutions was set to four levels each (100:0, 83.3:16.7, 66.7:33.3, and 50:50). As a result, the fresh weights of 83.3:16.7 and 66.7:33.3 were the greatest, and the leaf color was a healthy green. However, at 100:0 and 50:50 NO₃^-/NH₄⁺ ratios, the fresh weight was low, and leaf yellowing, tip burn, and leaf burn appeared. The nutrient solution with a 83.3:16.7 NO₃^-- to-NH₄⁺ ratio, which gave the greatest fresh weight in the second experiment, was chosen as the control, while the solution with a 50:50 NO₃^-/NH₄⁺ ratio with a lower nitrate content among the two unfavorable treatments was selected as a treatment group for the next experiment. In the third experiment, NH₄⁺ was partially replaced with urea to make four different ratios of NO₃^- to NH₄⁺ to urea (83:17:0, 50:50:0, 50:25:25, and 50:0:50) in combination with two levels of Si (0 and 10.7 mmol·L^-1 Si). The greatest fresh weight was obtained in the treatment in which the NO₃^-/NH₄⁺/urea ratio was 50:25:25. In particular, when Si was added to the solution, there was no decrease in the number of leaves, and plants with the greatest fresh weight, chlorophyll content, and leaf area were obtained. The number of leaves and leaf area are important indicators of high productivity since the Ssamchoo is used in ssam dishes. It can be concluded that a solution with a NO₃^-/NH₄⁺/urea ratio of 50:25:25 and supplemented with 10.7 mmol·L^-1 Si is the most suitable nutrient solution for growing Ssamchoo in the home hydroponic system developed.

A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning

BACKGROUND

Variety genuineness and purity are essential indices of maize seed quality that affect yield. However, detection methods for variety genuineness are time-consuming, expensive, require extensive training, or destroy the seeds in the process. Here, we present an accurate, high-throughput, cost-effective, and non-destructive method for screening variety genuineness that uses seed phenotype data with machine learning to distinguish between genetically and phenotypically similar seed varieties. Specifically, we obtained image data of seed morphology and hyperspectral reflectance for Jingke 968 and nine other closely-related varieties (non-Jingke 968). We then compared the robustness of three common machine learning algorithms in distinguishing these varieties based on the phenotypic imaging data.

RESULTS

Our results showed that hyperspectral imaging (HSI) combined with a multilayer perceptron (MLP) or support vector machine (SVM) model could distinguish Jingke 968 from varieties that differed by as few as two loci, with a 99% or higher accuracy, while machine vision imaging provided  ~ 90% accuracy. Through model validation and updating with varieties not included in the training data, we developed a genuineness detection model for Jingke 968 that effectively discriminated between genetically similar and distant varieties.

CONCLUSIONS

This strategy has potential for wide adoption in large-scale variety genuineness detection operations for internal quality control or governmental regulatory agencies, or for accelerating the breeding of new varieties. Besides, it could easily be extended to other target varieties and other crops.

Metabolomics Mechanism and Lignin Response to Laxogenin C, a Natural Regulator of Plants Growth

Laxogenin C (LGC) is a natural spirostanol deriving from plant hormone which has shown growing regulation similar to those of brassinosteroids. In the present study, LGC showed a promoting effect on tomato seed germination and seedling growth in a dose-dependent manner. We applied LC-MS/MS to investigate metabolome variations in the tomato treated with LGC, which revealed 10 differential metabolites (DMs) related to KEGG metabolites, associated with low and high doses of LGC. Enrichment and pathway mapping based on the KEGG database indicated that LGC regulated expressions of 2-hydroxycinnamic acid and l-phenylalanine to interfere with phenylalanine metabolism and phenylpropanoids biosynthesis. The two pathways are closely related to plant growth and lignin formation. In our further phenotypic verification, LGC was confirmed to affect seedling lignification and related phenylpropanoids, trans-ferulic acid and l-phenylalanine levels. These findings provided a metabolomic aspect on the plant hormone derivates and revealed the affected metabolites. Elucidating their regulation mechanisms can contribute to the development of sustainable agriculture. Further studies on agrichemical development would provide eco-friendly and efficient regulators for plant growth control and quality improvement.

The effects of different electrode materials on seed germination of Solanum nigrum L. and its Cd accumulation in soil

The effects of different electrode on Solanum nigrum L. seed germination were determined. The result showed that germination percentage (GP) of seeds in treatment T2 (titanium electrode) was 26.6% higher than in control (CK, without electric field). High potassium and calcium concentrations were beneficial for seed enzymatic activity in treatment T2, which could partly explain the increase in GP. Cd accumulation (μg/pot) in S. nigrum treated with any electric field was significantly higher (p<0.05) than in CK without electric field. Specifically, Cd accumulation under the treatment T3 (stainless steel electrode) was the highest both in roots and shoots; this accumulation in shoots and roots were 74.7 % and 67.4 % higher for stainless steel than in CK. This increase must have been associated with a higher Cd concentration in plants and did not exert a significant effect on the biomass. In particular, Cd concentrations in roots and shoots under stainless steel treatment were both significantly higher than in CK (p<0.05), which had to be related to the higher available Cd concentration in the soil in the middle region. Furthermore, it could be attributed to altered soil pH and other soil properties. Moreover, none of the biomasses were significantly affected (p<0.05) by different electrode materials compared to CK.

Modification of the effect of maturity variation on nondestructive detection of apple quality based on the compensation model

In this study, the effect of maturity variation on the prediction of the soluble solids content (SSC) and firmness of apples was determined using visible and near-infrared spectroscopy. In 2018, 520 apples from six ripening stages were collected. The single maturity model and multi-maturity model of SSC and firmness were established using partial least-squares regression. Apples at the same and different maturity stages were used to verify the developed model. Whereas the single maturity model was affected by maturity variation, the multi-maturity model could accurately predict the SSC and firmness of apples at different maturity stages. The multi-maturity model developed based on six maturity calibration sets had the best predictive performance. The root mean square error of prediction (RMSEP) of SSC and firmness was 0.614-0.802 °Brix and 0.402-0.650 kg/cm², respectively. The long-term performance of the optimal multi-maturity model was evaluated using validation sets. The predictive performance was decreased and the RMSEP increased when the model was used to predict the SSC and firmness of apples in different seasons. The predictive performance of the model was improved after slope/bias (S/B) correction, and the RMSEP of SSC and firmness decreased to 0.405-0.587°Brix and 0.518-0.628 kg/cm² respectively. Overall, the multi-maturity model eliminated the effect of maturity variation, and the multi-maturity model coupled with S/B correction permitted the rapid and accurate detection of the SSC and firmness of apples at different maturity stages and in different seasons.

Optimizing Temperature and Photoperiod in a Home Cultivation System to Program Normal, Delayed, and Hastened Growth and Development Modes for Leafy Oak-Leaf and Romaine Lettuces

As the risk of open-field cultivation increases with climate change, some analysts say that the day when ordinary vegetables will be produced at home is not far away. Moreover, due to the recent coronavirus outbreak, outdoor activities are becoming difficult, leisure activities that can be done at home have become more necessary, and the demand for home gardening has increased. This study was conducted to improve the technology for hydroponics at home. We experimented with whether the harvest time can be hastened or delayed by environmentally controlling the growing season, and what conditions are appropriate. Experiments were conducted with leafy vegetables (Lactuca sativa L. ‘Oak-leaf’ and Lactuca sativa L. var. longifolia, or romaine) that can easily be grown in a closed plant cultivator in which the external air can circulate, and the temperature/photoperiod can be controlled. Two settings for the temperature (25/18 °C and 20/15 °C; day/night) and three settings for the photoperiod (10, 14, and 18 hours; day/night) were employed. It took a total of four weeks from sowing to harvest, and the appropriate harvest time was predicted from the yield. As a result, although there was a difference depending on the vegetable variety, a temperature setting of 25/18 °C and a photoperiod of 14 hours were the most suitable for hastened growth, and a 20/15 °C temperature and 18 hours of photoperiod were suitable for the delayed growth.

Technical non-invasive study of the novo-hispanic painting the Pentecost by Baltasar de Echave Orio by spectroscopic techniques and hyperspectral imaging: In quest for the painter's hand

This paper presents a combination of noninvasive techniques for the study of superimposed painting stages in The Pentecost, a Mexican Colonial panel painting attributed to Baltasar de Echave Orio (1558 - 1619). The application of reflected hyperspectral imaging (HSI) analysis for mapping the distribution of the pigments in the paint surface and the use of ultraviolet (UV) fluorescence photography and X-ray radiography as complementary imaging techniques provide new insights into the making process of the artwork, its manufacturing and conservation state. For a better understanding of the in situ results gathered, we studied a series of paint mock-up samples created following recipes and studio practices from art treatises. The use of spot analytical methods such as fiber optic reflectance spectroscopy (FORS) and X-ray fluorescence spectroscopy (XRF) allowed for a robust identification of the artist's materials.

Potential of smartphone-coupled micro NIR spectroscopy for quality control of green tea

Green tea adulterated with sugar and glutinous rice flour has an increased sensitivity to water, which affects the safety of the tea. A total of 475 samples of pure tea, sugar-adulterated tea, and glutinous-rice-flour-adulterated tea were prepared and scanned using micro near infrared spectroscopy (NIRS). The collected NIRS data were qualitatively and quantitatively detected by a multi-layer algorithm model. Principal component analysis indicated that the three sample groups had an obvious separation trend. The discriminate rate of the optimal qualitative model, namely support vector machine, was 97.47% for the prediction set. A total of three wavelength selection methods were used to improve the performances of partial least squares regression and support vector machine regression (SVR) models. The nonlinear SVR models based on characteristic wavelengths selected by iteratively retaining informative variables algorithm provided satisfactory results for the identification of sugar and glutinous rice flour adulteration. The correlation coefficients for prediction (Rp) were >0.94, and the residual prediction deviation were >3. The results indicated that smartphone-based micro NIRS can be effectively used to qualitatively and quantitatively analyze adulterants in green tea.

Rapid determination of lignocellulose in corn stover based on near-infrared reflectance spectroscopy and chemometrics methods

In this study, a rapid detection method based on near-infrared reflectance spectroscopy was proposed for measuring the contents of cellulose, hemicellulose and lignin in corn stover. In the basis of strategies of variable selection, feature extraction and nonlinear modeling, BiPLS-PCA-SVM was constructed using backward interval partial least squares combined with principal component analysis and support vector machine, which was used to improve the performance of spectral regression calibration model. For BiPLS-PCA-SVM model, the determination coefficients, root mean squared error and residual predictive deviation for the validation set were 0.906, 0.900% and 3.213 for cellulose; 0.987, 0.797% and 9.071 for hemicellulose; and 0.936, 0.264% and 4.024 for lignin, correspondingly. The results indicate that near-infrared reflectance spectroscopy combined with BiPLS-PCA-SVM can provide a reliable alternative strategy to detect contents of lignocellulosic components for pretreated corn stover in the anaerobic digestion process.

Advances in the Identification of Quantitative Trait Loci and Genes Involved in Seed Vigor in Rice

Seed vigor is a complex trait, including the seed germination, seedling emergence, and growth, as well as seed storability and stress tolerance, which is important for direct seeding in rice. Seed vigor is established during seed development, and its level is decreased during seed storage. Seed vigor is influenced by genetic and environmental factors during seed development, storage, and germination stages. A lot of factors, such as nutrient reserves, seed dying, seed dormancy, seed deterioration, stress conditions, and seed treatments, will influence seed vigor during seed development to germination stages. This review highlights the current advances on the identification of quantitative trait loci (QTLs) and regulatory genes involved in seed vigor at seed development, storage, and germination stages in rice. These identified QTLs and regulatory genes will contribute to the improvement of seed vigor by breeding, biotechnological, and treatment approaches.