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Background: Seed vigor identification is critical to guaranteeing the quality and yield of maize. Although seeds with impaired vigor may germinate under normal conditions, planting under unfavorable conditions makes it difficult to produce healthy plants. Therefore, non-destructive and rapid detection of seed vigor using hyperspectral imaging (HSI) technology is crucial for improving crop production efficiency. Methods: Hyperspectral images of maize seeds were acquired employing the HSI system, the original spectra were preprocessed using Savitzky–Golay smoothing and multiplicative scatter correction, and the feature wavelengths were extracted using the successive projections algorithm (SPA). Discriminant models were constructed based on support vector machine (SVM), random forest, artificial neural network (ANN), and convolutional neural network (CNN-DC). Results: The results showed that SVM, ANN, and CNN-DC could discriminate well between maize seeds with different vigor levels, and their accuracy rate was over 70%. The SPA algorithm showed that the RMSE value achieved a minimum of 0.3406, while the number of variables was 49. The CNN-DC model outperformed the other models, which reached the highest accuracy of 92.06%. This study demonstrates that DL combined with HSI has excellent potential for identifying seed vigor. Conclusions: This study shows that the proposed method has excellent results for hyperspectral image data processing and can accurately identify maize seed vigor. [ABSTRACT FROM AUTHOR]

Abstract Background Seed vigor identification is critical to guaranteeing the quality and yield of maize. Although seeds with impaired vigor may germinate under normal conditions, planting under unfavorable conditions makes it difficult to produce healthy plants. Therefore, non-destructive and rapid detection of seed vigor using hyperspectral imaging (HSI) technology is crucial for improving crop production efficiency. Methods Hyperspectral images of maize seeds were acquired employing the HSI system, the original spectra were preprocessed using Savitzky–Golay smoothing and multiplicative scatter correction, and the feature wavelengths were extracted using the successive projections algorithm (SPA). Discriminant models were constructed based on support vector machine (SVM), random forest, artificial neural network (ANN), and convolutional neural network (CNN-DC). Results The results showed that SVM, ANN, and CNN-DC could discriminate well between maize seeds with different vigor levels, and their accuracy rate was over 70%. The SPA algorithm showed that the RMSE value achieved a minimum of 0.3406, while the number of variables was 49. The CNN-DC model outperformed the other models, which reached the highest accuracy of 92.06%. This study demonstrates that DL combined with HSI has excellent potential for identifying seed vigor. Conclusions This study shows that the proposed method has excellent results for hyperspectral image data processing and can accurately identify maize seed vigor.

Seed endophytes in maize, which facilitate the transmission of microorganisms from one plant generation to the next, may play a crucial role in plant protection and growth promotion. This study aimed to investigate the effects of various maize varieties on the communities of endophytic bacteria in seeds and germinating roots. This study utilized Illumina high-throughput sequencing technology to examine the structural and diversity differences of endophytic bacterial communities within seed maize (BY1507), silage maize (QQ446), and wild maize (Teosinte) in both seeds and germinating roots. The results showed that 416 bacterial genera were detected, with Pantoea, Lachnospiraceae, Pararhizobium, Enterobacteriaceae, Stenotrophomonas, and Pseudonocardia being the most prevalent (relative abundance > 10%) at the genus level. No significant difference was observed in diversity indices (Chao1, ACE, Shannon, and Simpson) of seed endophytes among BY1507, QQ446, and Teosinte. The Shannon and Simpson indices for the germinating root endophyte from the wild variety (Teosinte) were significantly higher than the domesticated varieties (BY1507 and QQ446). PCoA revealed a notable overlap in the endophytic bacterial communities from the seeds of BY1507, QQ446, and Teosinte. Yet, clustering patterns were found. Co-occurrence network analysis showed that BY1507, QQ446, and Teosinte share a notable proportion of shared endophytic bacteria (>30%) between the seeds and germinating roots. This investigation elucidates the characteristics of endophytic microbial communities of seeds and germinating roots with seed maize, silage maize, and wild maize, offering data for future research on the physiological ecological adaptation of these endophytic microbial communities. [ABSTRACT FROM AUTHOR]

The harvest year of maize seeds has a significant impact on seed vitality and maize yield. Therefore, it is vital to identify new seeds. In this study, an on-line near-infrared (NIR) spectra collection device (899–1715 nm) was designed and employed for distinguishing maize seeds harvested in different years. Compared with least squares support vector machine (LS-SVM), k-nearest neighbor (KNN), and extreme learning machine (ELM), the partial least squares discriminant analysis (PLS-DA) model has the optimal recognition performance for maize seed harvest years. Six different preprocessing methods, including Savitzky–Golay smoothing (SGS), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky–Golay 1 derivative (SG-D1), Savitzky–Golay 2 derivative (SG-D2), and normalization (Norm), were used to improve the quality of the spectra. The Monte Carlo cross-validation uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), bootstrapping soft shrinkage (BOSS), successive projections algorithm (SPA), and their combinations were used to obtain effective wavelengths and decrease spectral dimensionality. The MC-UVE-BOSS-PLS-DA model achieved the classification with an accuracy of 88.75% using 93 features based on Norm preprocessed spectral data. This study showed that the self-designed NIR collection system could be used to identify the harvested years of maize seed. [ABSTRACT FROM AUTHOR]

Abstract The purity of the seeds is one of the important factors that increase the yield. For this reason, the classification of maize cultivars constitutes a significant problem. Within the scope of this study, six different classification models were designed to solve this problem. A special dataset was created to be used in the models designed for the study. The dataset contains a total of 14,469 images in four classes. Images belong to four different maize types, BT6470, CALIPOS, ES_ARMANDI, and HIVA, taken from the BIOTEK company. AlexNet and ResNet50 architectures, with the transfer learning method, were used in the models created for the image classification. In order to improve the classification success, LSTM (Directional Long Short‐Term Memory) and BiLSTM (Bi‐directional Long Short‐Term Memory) algorithms and AlexNet and ResNet50 architectures were hybridized. As a result of the classifications, the highest classification success was obtained from the ResNet50+BiLSTM model with 98.10%.

Ensuring the security of germplasm resources is of great significance for the sustainable development of agriculture and ecological balance. By combining the morphological characteristics of maize seeds with hyperspectral data, maize variety classification has been achieved using machine learning algorithms. Initially, the morphological data of seeds are obtained from images, followed by the selection of feature subsets using Recursive Feature Elimination (RFE) and Select From Model (SFM) methods, indicating that features selected by RFE exhibit better performance in maize seed classification. For hyperspectral data (350–2500 nm), Competitive Adaptive Re-weighted Sampling (CARS) and the Successive Projections Algorithm (SPA) are employed to extract feature wavelengths, with the SPA algorithm demonstrating superior performance in maize seed classification tasks. Subsequently, the two sets of data are merged, and a Random Forest (RF) classifier optimized by Grey Wolf Optimization (GWO) is utilized. Given the limitations of GWO, strategies such as logistic chaotic mapping for population initialization, random perturbation, and final replacement mechanisms are incorporated to enhance the algorithm's search capabilities. The experimental results show that the proposed ZGWO-RF model achieves an accuracy of 95.9%, precision of 96.2%, and recall of 96.1% on the test set, outperforming the unimproved model. The constructed model exhibits improved identification effects on multi-source data, providing a new tool for non-destructive testing and the accurate classification of seeds in the future. [ABSTRACT FROM AUTHOR]

The purity of the seeds is one of the important factors that increase the yield. For this reason, the classification of maize cultivars constitutes a significant problem. Within the scope of this study, six different classification models were designed to solve this problem. A special dataset was created to be used in the models designed for the study. The dataset contains a total of 14,469 images in four classes. Images belong to four different maize types, BT6470, CALIPOS, ES_ARMANDI, and HIVA, taken from the BIOTEK company. AlexNet and ResNet50 architectures, with the transfer learning method, were used in the models created for the image classification. In order to improve the classification success, LSTM (Directional Long Short‐Term Memory) and BiLSTM (Bi‐directional Long Short‐Term Memory) algorithms and AlexNet and ResNet50 architectures were hybridized. As a result of the classifications, the highest classification success was obtained from the ResNet50+BiLSTM model with 98.10%. [ABSTRACT FROM AUTHOR]

Peanuts and maize, both known highly susceptible to mycotoxin producing fungi are significant component of local diet. These micro organisms especially Aspergillus flavus can reduce the yield due to contamination of the crop with production of aflatoxin. Bitter plant extract like Azadirachta indica is favoured as biotic control because it has no side effect and its relatively cheap and easily available in our surroundings. The maximum % inhibition at 2.5 ml of Azadirachta indica recorded in maize seeds (95 %) and peanut (92 %) at 2.0 and 2.4 ppm concentration of Aflatoxin Bi after 7 days, respectively. Whereas minimum % inhibition at 0.5 ml of Azadirachta indica were recorded in maize seeds (10 %) and peanut (21 %) at 2.0 and 2.4 ppm concentration of Aflatoxin Bi after 3 days, respectively. Our findings concluded that the Azadirachta indica control the growth of Aspergillus flavus and aflatoxin production i.e., means control the diseases by microorganisms. [ABSTRACT FROM AUTHOR]

Seed endophytes in maize, which facilitate the transmission of microorganisms from one plant generation to the next, may play a crucial role in plant protection and growth promotion. This study aimed to investigate the effects of various maize varieties on the communities of endophytic bacteria in seeds and germinating roots. This study utilized Illumina high-throughput sequencing technology to examine the structural and diversity differences of endophytic bacterial communities within seed maize (BY1507), silage maize (QQ446), and wild maize (Teosinte) in both seeds and germinating roots. The results showed that 416 bacterial genera were detected, with Pantoea, Lachnospiraceae, Pararhizobium, Enterobacteriaceae, Stenotrophomonas, and Pseudonocardia being the most prevalent (relative abundance > 10%) at the genus level. No significant difference was observed in diversity indices (Chao1, ACE, Shannon, and Simpson) of seed endophytes among BY1507, QQ446, and Teosinte. The Shannon and Simpson indices for the germinating root endophyte from the wild variety (Teosinte) were significantly higher than the domesticated varieties (BY1507 and QQ446). PCoA revealed a notable overlap in the endophytic bacterial communities from the seeds of BY1507, QQ446, and Teosinte. Yet, clustering patterns were found. Co-occurrence network analysis showed that BY1507, QQ446, and Teosinte share a notable proportion of shared endophytic bacteria (>30%) between the seeds and germinating roots. This investigation elucidates the characteristics of endophytic microbial communities of seeds and germinating roots with seed maize, silage maize, and wild maize, offering data for future research on the physiological ecological adaptation of these endophytic microbial communities.

The harvest year of maize seeds has a significant impact on seed vitality and maize yield. Therefore, it is vital to identify new seeds. In this study, an on-line near-infrared (NIR) spectra collection device (899–1715 nm) was designed and employed for distinguishing maize seeds harvested in different years. Compared with least squares support vector machine (LS-SVM), k-nearest neighbor (KNN), and extreme learning machine (ELM), the partial least squares discriminant analysis (PLS-DA) model has the optimal recognition performance for maize seed harvest years. Six different preprocessing methods, including Savitzky–Golay smoothing (SGS), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), Savitzky–Golay 1 derivative (SG-D1), Savitzky–Golay 2 derivative (SG-D2), and normalization (Norm), were used to improve the quality of the spectra. The Monte Carlo cross-validation uninformative variable elimination (MC-UVE), competitive adaptive reweighted sampling (CARS), bootstrapping soft shrinkage (BOSS), successive projections algorithm (SPA), and their combinations were used to obtain effective wavelengths and decrease spectral dimensionality. The MC-UVE-BOSS-PLS-DA model achieved the classification with an accuracy of 88.75% using 93 features based on Norm preprocessed spectral data. This study showed that the self-designed NIR collection system could be used to identify the harvested years of maize seed.

Using traditional detection technology to meet the requirements of rapid, non-destructive, and effective seed quality detection is challenging. As science and technology advance, spectral imaging technology in conjunction with chemometric methods is now able to get both the spectral and image information of the analyte, offering the benefits of speed, non- destructiveness, and high efficiency. The method is currently applied to all aspects of seed quality detection technology. This paper introduced the theory of spectral imaging technology, summarized the status of maize seeds, the method of spectral data preprocessing, the method of extracting the characteristic wavelength of spectral data, the modeling analysis method and the model evaluation index in the detection process of spectral imaging technology. It also reviewed the research progress of spectral imaging technology in detecting maize seed vitality, moisture, disease, quality and origin. In the end, considering the problems of low data processing efficiency and low model accuracy in the quality detection of maize seeds using spectral imaging technology, the development trend of spectral imaging technology was prospected from the perspectives of research systematization and fusion of new algorithms, in order to provide references for non-destructive testing of maize seeds. [ABSTRACT FROM AUTHOR]

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 singleseed 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. [ABSTRACT FROM AUTHOR]

An appropriate contact mechanics model and parameters are key to achieving accurate results in discrete element analyzis. This is necessary to predict the process of contact collision between the soil and maize seed during deposition. In this paper, the contact process between maize seed and soil is analyzed using the maize seed variety (Liangyu 99) and maize-sowing field soil (with three different moisture contents) as research objects. Based on this, the contact process between maize seeds and soil has been analyzed, on the basis of which a mechanical model suitable for simulating the contact process between maize seeds and soil has been explored, and the selection of parameters between heterogeneous particles (maize seed and soil particles) has been investigated. The results showed that adhesion forces have a significant effect on the collision process between seed and soil particles. While the presence of tangential adhesion force can be replaced by increasing the static and rolling friction coefficients, the normal adhesion force cannot be compensated in this way. The Edinburgh Elasto-Plastic Adhesive (EEPA) model is selected in this paper to describe the contact between seed and soil particles. The significance of the input parameters in the EEPA model is investigated using the Plackett–Burman test. The parameters between soil and seed particles are optimized using the central composite design, and the optimal parameter combinations are obtained. The relative error between the simulation and test result of the slope test for the three soil moisture contents is within 5.4%, validating the accuracy of the calibrated parameters. [ABSTRACT FROM AUTHOR]

Ensuring the security of germplasm resources is of great significance for the sustainable development of agriculture and ecological balance. By combining the morphological characteristics of maize seeds with hyperspectral data, maize variety classification has been achieved using machine learning algorithms. Initially, the morphological data of seeds are obtained from images, followed by the selection of feature subsets using Recursive Feature Elimination (RFE) and Select From Model (SFM) methods, indicating that features selected by RFE exhibit better performance in maize seed classification. For hyperspectral data (350–2500 nm), Competitive Adaptive Re-weighted Sampling (CARS) and the Successive Projections Algorithm (SPA) are employed to extract feature wavelengths, with the SPA algorithm demonstrating superior performance in maize seed classification tasks. Subsequently, the two sets of data are merged, and a Random Forest (RF) classifier optimized by Grey Wolf Optimization (GWO) is utilized. Given the limitations of GWO, strategies such as logistic chaotic mapping for population initialization, random perturbation, and final replacement mechanisms are incorporated to enhance the algorithm’s search capabilities. The experimental results show that the proposed ZGWO-RF model achieves an accuracy of 95.9%, precision of 96.2%, and recall of 96.1% on the test set, outperforming the unimproved model. The constructed model exhibits improved identification effects on multi-source data, providing a new tool for non-destructive testing and the accurate classification of seeds in the future.

Iron is a micronutrient essential for optimal plant development, nevertheless, due to their low solubility it is poorly available to plants, affecting many agronomically important crops. Nanofertilizers are nanoparticles that have been used as an alternative for the release of micro and macronutrients necessary for plant nutrition. Therefore, in this research we evaluated the effect of the application of aqueous suspensions of iron oxide (magnetite-maghemite) nanoparticles stabilized with chitosan on the germination and growth of Maize (Zea mays L.) and Lettuce (Lactuca sativa L.) plants at in vitro level. Preliminary results suggest that for aqueous nanoparticle suspensions with iron contents below 50 ppm there is no evidence of negative effects on the seed germination process and on the growth of maize seedlings. Similarly, it was determined that there is no phytotoxic effect of the suspensions on Lettuce seeds. [ABSTRACT FROM AUTHOR]

Maize accumulates large amounts of starch in seeds which have been used as food for human and animals. Maize starch is an importantly industrial raw material for bioethanol production. One critical step in bioethanol production is degrading starch to oligosaccharides and glucose by α-amylase and glucoamylase. This step usually requires high temperature and additional equipment, leading to an increased production cost. Currently, there remains a lack of specially designed maize cultivars with optimized starch (amylose and amylopectin) compositions for bioethanol production. We discussed the features of starch granules suitable for efficient enzymatic digestion. Thus far, great advances have been made in molecular characterization of the key proteins involved in starch metabolism in maize seeds. The review explores how these proteins affect starch metabolism pathway, especially in controlling the composition, size and features of starch. We highlight the roles of key enzymes in controlling amylose/amylopectin ratio and granules architecture. Based on current technological process of bioethanol production using maize starch, we propose that several key enzymes can be modified in abundance or activities via genetic engineering to synthesize easily degraded starch granules in maize seeds. The review provides a clue for developing special maize cultivars as raw material in the bioethanol industry. [ABSTRACT FROM AUTHOR]

Iron is a micronutrient essential for optimal plant development, nevertheless, due to their low solubility it is poorly available to plants, affecting many agronomically important crops. Nanofertilizers are nanoparticles that have been used as an alternative for the release of micro and macronutrients necessary for plant nutrition. Therefore, in this research we evaluated the effect of the application of aqueous suspensions of iron oxide (magnetite-maghemite) nanoparticles stabilized with chitosan on the germination and growth of Maize (Zea mays L.) and Lettuce (Lactuca sativa L.) plants at in vitro level. Preliminary results suggest that for aqueous nanoparticle suspensions with iron contents below 50 ppm there is no evidence of negative effects on the seed germination process and on the growth of maize seedlings. Similarly, it was determined that there is no phytotoxic effect of the suspensions on Lettuce seeds.

The main goals of the present investigation were to develop O/W nanoemulsion fungicides based on cold-pressed Nigella sativa (black seed) oil to prevent Penicillium verrucosum infection of maize seeds and to test their antifungal activity against this fungus. Additionally, the effect of these nanoemulsions on plant physiological parameters was also investigated. Two nonionic surfactants namely Tween 20 and Tween 80 were used as emulsifying agents in these formulations. The effect of sonication time and surfactant type on the mean droplet size, polydispersity index (PDI), and zeta potential of the nanoemulsions were determined by dynamic light scattering (DLS). Results indicated that both sonication time and emulsifier type had pronounced effects on the stability of O/W nanoemulsions with a small particle size range (168.6–345.3 nm), acceptable PDI (0.181–0.353), and high zeta potential (−27.24 to –48.82 mV). Tween 20 showed superior stability compared to Tween 80 nanoemulsions. The in vitro results showed that complete inhibition of P. verrucosum-growth was obtained by 10_T80 and 10_T20 nanoemulsions at 100% concentration. All nanoemulsions had increment effects on maize seed germination by 101% in the case of 10_T20 and 10_T80 compared to untreated seeds or the chemical fungicide treatment. Nanoemulsions (10_T20 and 10_T80) were able to stimulate root and shoot length as compared to the fungicide treatment. Seed treatment with 10_T80 nanoemulsion showed the highest AI and protease activity by 75 and 70%, respectively, as compared to the infected control. The produced nanoemulsions might provide an effective protectant coating layer for the stored maize seeds. [ABSTRACT FROM AUTHOR]

The irregular shape, varying internal compositions, and uneven distributions of maize seeds can degrade the metering performance of maize seeders. This study investigates the effect of irregular maize on the performance of a high-filling-rate seed metering device using DEM. The results show that the seed population in the seed box formed large and small cycles of seed transport due to the disturbances caused by the three wheels. The increasing angular speed of the three wheels reduced the rate of seed filling in the seed filling regions by gravity and disturbances. In addition, an analysis of the repose angle formed by the dropped seed indicated that the angular speed of the taking seed wheel has a certain effect. The large angular speed reduced the coefficient of variation of the seed drop location. The repose angle of the horse tooth seed was greater than those of the other. Meanwhile, the irregular maize seed can also reduce the coefficient of variation of the seed drop location. A comparison between the simulation and experiment results indicates the number of metering seed were basically the same. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Seeds are the most fundamental and significant production tool in agriculture. They play a critical role in boosting the output and revenue of agriculture. To achieve rapid identification and protection of maize seeds, 3938 images of 11 different types of maize seeds were collected for the experiment, along with a combination of germ and non-germ surface datasets. The training set, validation set, and test set were randomly divided by a ratio of 7:2:1. The experiment introduced the CBAM (Convolutional Block Attention Module) attention mechanism into MobileNetV2, improving the CBAM by replacing the cascade connection with a parallel connection, thus building an advanced mixed attention module, I_CBAM, and establishing a new model, I_CBAM_MobileNetV2. The proposed I_CBAM_MobileNetV2 achieved an accuracy of 98.21%, which was 4.88% higher than that of MobileNetV2. Compared to Xception, MobileNetV3, DenseNet121, E-AlexNet, and ResNet50, the accuracy was increased by 9.24%, 6.42%, 3.85%, 3.59%, and 2.57%, respectively. Gradient-Weighted Class Activation Mapping (Grad-CAM) network visualization demonstrates that I_CBAM_MobileNetV2 focuses more on distinguishing features in maize seed images, thereby boosting the accuracy of the model. Furthermore, the model is only 25.1 MB, making it suitable for portable deployment on mobile terminals. This study provides effective strategies and experimental methods for identifying maize seed varieties using deep learning technology. This research provides technical assistance for the non-destructive detection and automatic identification of maize seed varieties. [ABSTRACT FROM AUTHOR]

An appropriate contact mechanics model and parameters are key to achieving accurate results in discrete element analyzis. This is necessary to predict the process of contact collision between the soil and maize seed during deposition. In this paper, the contact process between maize seed and soil is analyzed using the maize seed variety (Liangyu 99) and maize-sowing field soil (with three different moisture contents) as research objects. Based on this, the contact process between maize seeds and soil has been analyzed, on the basis of which a mechanical model suitable for simulating the contact process between maize seeds and soil has been explored, and the selection of parameters between heterogeneous particles (maize seed and soil particles) has been investigated. The results showed that adhesion forces have a significant effect on the collision process between seed and soil particles. While the presence of tangential adhesion force can be replaced by increasing the static and rolling friction coefficients, the normal adhesion force cannot be compensated in this way. The Edinburgh Elasto-Plastic Adhesive (EEPA) model is selected in this paper to describe the contact between seed and soil particles. The significance of the input parameters in the EEPA model is investigated using the Plackett–Burman test. The parameters between soil and seed particles are optimized using the central composite design, and the optimal parameter combinations are obtained. The relative error between the simulation and test result of the slope test for the three soil moisture contents is within 5.4%, validating the accuracy of the calibrated parameters.

The main goals of the present investigation were to develop O/W nanoemulsion fungicides based on cold-pressed Nigella sativa (black seed) oil to prevent Penicillium verrucosum infection of maize seeds and to test their antifungal activity against this fungus. Additionally, the effect of these nanoemulsions on plant physiological parameters was also investigated. Two nonionic surfactants namely Tween 20 and Tween 80 were used as emulsifying agents in these formulations. The effect of sonication time and surfactant type on the mean droplet size, polydispersity index (PDI), and zeta potential of the nanoemulsions were determined by dynamic light scattering (DLS). Results indicated that both sonication time and emulsifier type had pronounced effects on the stability of O/W nanoemulsions with a small particle size range (168.6–345.3 nm), acceptable PDI (0.181–0.353), and high zeta potential (−27.24 to –48.82 mV). Tween 20 showed superior stability compared to Tween 80 nanoemulsions. The in vitro results showed that complete inhibition of P. verrucosum-growth was obtained by 10_T80 and 10_T20 nanoemulsions at 100% concentration. All nanoemulsions had increment effects on maize seed germination by 101% in the case of 10_T20 and 10_T80 compared to untreated seeds or the chemical fungicide treatment. Nanoemulsions (10_T20 and 10_T80) were able to stimulate root and shoot length as compared to the fungicide treatment. Seed treatment with 10_T80 nanoemulsion showed the highest AI and protease activity by 75 and 70%, respectively, as compared to the infected control. The produced nanoemulsions might provide an effective protectant coating layer for the stored maize seeds.

Abstract Background Widely targeted metabolomics was applied to estimate the differences in the metabolite profiles of maize seeds from 3 natural genotypic varieties and 4 genetically modified (GM) lines. Results Pairwise comparison with their isogenic controls revealed 71, 121, 43 and 95 differentially accumulated metabolites (DAMs) in GM maize seeds of C0030.2.4, C0030.3.5, C0010.1.1 and C0010.3.1, respectively. KEGG pathway enrichment analysis showed that most of these DAMs participated in the biosynthesis of secondary metabolites and purine metabolism in GM maize C0030.2.4 and C0030.3.5, but participated in tryptophan metabolism and 2-oxocarboxylic acid metabolism in C0010.3.1 seeds and in metabolic pathways and the biosynthesis of secondary metabolites in C0010.1.1 seeds. The data also showed that the differences in metabolite accumulation, both total DAMs and co-DAMs, among the different natural genotypic varieties (418 DAMs and 39 co-DAMs) were greater than those caused by genetic modification (330 DAMs and 3 co-DAMs). Conclusions None of the DAMs were identified as new or unintended, showing only changes in abundance in the studied maize seeds. The metabolite profile differences among the 3 non-GM lines were more notable than those among GM lines. Different genetic backgrounds affect metabolite profiling more than gene modification itself. Graphic abstract

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. [ABSTRACT FROM AUTHOR]

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.

Premium maize varieties are the focus of attention of farmers, breeders, food manufacturers, and people in other industries. Maize seed fraud causes huge financial losses to these industries and many varieties are difficult to distinguish due to their similar appearance. Hyperspectral imaging, as a powerful tool for rapid non-destructive testing, combined with traditional pattern recognition/classification algorithms, has had many successful reports in seed variety identification. In practice, however, fake varieties are too complex to enumerate and a new fake variety may be developed at any time, posing a serious challenge to existing variety identification models. In view of this, this paper proposes a deep one-class learning (OCL) network for seed fraud detection. Specifically, it trains a hypersphere with minimum-volume that can enclose the real variety and isolate all fake varieties outside the hypersphere. In order to improve the performance and stability of the model, the spectral and spatial information of seeds are fused, and a band attention module is used to amplify the weights of the effective bands to suppress the interference from redundant bands. The experimental results show that our method has a mean accuracy of 93.70% for receiving real varieties and 94.28% for rejecting fake varieties, which is superior to several existing state-of-the-art OCL models. • Hyperspectral imaging combined with one-class learning for seed fraud detection. • A one-class classifier (OCC) base on spatial and spectral information is proposed. • The OCC trains a hypersphere to receive the real variety and reject fake varieties. • Experimental results show that our method is more stable and performs better. • Our method is also suitable for fraud detection of other food and agro-products. [ABSTRACT FROM AUTHOR]

• A new automatic maize seeding machine was developed and tested. • YOLOV5 combined with merge-NMS, with 960 × 960 images, can accurately detect maize seeding rate. • YOLOV5 with merge-NMS is robust for processing images under different illumination. • The seeding distribution can be correctly evaluated with an average accuracy of 91.14%. Seeding is a time-consuming and labour-intensive process in the detection of maize seeds germination rate. Automatic seeding can reduce labour requirements and improve efficiency. The quality of automatic seeding plays an important role in collecting accurate germination rates. In this research, a newly developed automatic maize seeding machine (AMSM) was used to detect the maize seeds and evaluate distribution by advanced computer vision and analysis methodologies. Three methods were utilized to detect maize seeds after they were loaded into the holes of the seed plate of AMSM: colour segmentation + connected component analysis, support vector machine (SVM) + sliding window, and YOLOV5. The results showed that compared with colour + connected component analysis and sliding window + SVM, YOLOV5 exhibited higher accuracy and improved computational efficiency. When the input image pixel size was 960 × 960, the precision of YOLOV5 + merge-NMS was 97.3 %, the recall was 0.918, the F1-score was 0.945, and the processing time of a single image was 0.38 ms, which is the optimal performance. YOLOV5 + merge-NMS demonstrated robust detection maize seeds in images with different illumination equalization processing. Based on maize seeds detection results, seeding distribution evaluation was realized with an average accuracy of 91.14 %. In addition, the normal seeding of the AMSM was 82.3 %. Utilization of YOLOV5 + merge-NMS enables rapid and precise detection of maize seeding while establishing a basis for accurately calculating seed distribution. The AMSM can meet the requirements of automatic maize seeds detection and distribution evaluation and the developed methodologies have the potential for automatic germination detection. [ABSTRACT FROM AUTHOR]

Maize accumulates large amounts of starch in seeds which have been used as food for human and animals. Maize starch is an importantly industrial raw material for bioethanol production. One critical step in bioethanol production is degrading starch to oligosaccharides and glucose by α-amylase and glucoamylase. This step usually requires high temperature and additional equipment, leading to an increased production cost. Currently, there remains a lack of specially designed maize cultivars with optimized starch (amylose and amylopectin) compositions for bioethanol production. We discussed the features of starch granules suitable for efficient enzymatic digestion. Thus far, great advances have been made in molecular characterization of the key proteins involved in starch metabolism in maize seeds. The review explores how these proteins affect starch metabolism pathway, especially in controlling the composition, size and features of starch. We highlight the roles of key enzymes in controlling amylose/amylopectin ratio and granules architecture. Based on current technological process of bioethanol production using maize starch, we propose that several key enzymes can be modified in abundance or activities via genetic engineering to synthesize easily degraded starch granules in maize seeds. The review provides a clue for developing special maize cultivars as raw material in the bioethanol industry.

Background: Widely targeted metabolomics was applied to estimate the differences in the metabolite profiles of maize seeds from 3 natural genotypic varieties and 4 genetically modified (GM) lines. Results: Pairwise comparison with their isogenic controls revealed 71, 121, 43 and 95 differentially accumulated metabolites (DAMs) in GM maize seeds of C0030.2.4, C0030.3.5, C0010.1.1 and C0010.3.1, respectively. KEGG pathway enrichment analysis showed that most of these DAMs participated in the biosynthesis of secondary metabolites and purine metabolism in GM maize C0030.2.4 and C0030.3.5, but participated in tryptophan metabolism and 2-oxocarboxylic acid metabolism in C0010.3.1 seeds and in metabolic pathways and the biosynthesis of secondary metabolites in C0010.1.1 seeds. The data also showed that the differences in metabolite accumulation, both total DAMs and co-DAMs, among the different natural genotypic varieties (418 DAMs and 39 co-DAMs) were greater than those caused by genetic modification (330 DAMs and 3 co-DAMs). Conclusions: None of the DAMs were identified as new or unintended, showing only changes in abundance in the studied maize seeds. The metabolite profile differences among the 3 non-GM lines were more notable than those among GM lines. Different genetic backgrounds affect metabolite profiling more than gene modification itself. [ABSTRACT FROM AUTHOR]

Moisture content (MC) is one of the important factors to assess the quality of maize seeds. In this study, the feasibility of using long-wave near infrared (LWNIR) hyperspectral imaging (HSI) technique with the spectral range of 930-2548 nm for predicting MC of single maize seeds was observed. The averaged spectra extracted from whole and centroid regions in the embryo side of single maize seeds were pretreated by Savizky-Golay smoothing and first derivative (SG-D1). A combination of uninformative variable elimination (UVE) and successive projections algorithm (SPA) was proposed to select feature wavelengths (variables) from LWNIR hyperspectral data. The quantitative relationship between feature wavelengths and MC was established by partial least square (PLSR) and least square-support vector machine (LS-SVM), respectively. Results illustrated that the UVE-SPA-LS-SVM model established based on spectra of centroid region obtained the best performance for MC detection of the single maize seeds. The correlation coefficient of prediction (Rpre) and root mean square error of prediction (RMSEP) were 0.9325 and 1.2109, respectively. Finally, MC distribution of single maize seed was visualized by pseudo-color map. This study showed LWNIR HSI technique was feasible to measure MC of single maize seeds and a robust and accurate model could be established based on UVE-SPA-LS-SVM method with the spectra of centroid regions.

Seeds are the most fundamental and significant production tool in agriculture. They play a critical role in boosting the output and revenue of agriculture. To achieve rapid identification and protection of maize seeds, 3938 images of 11 different types of maize seeds were collected for the experiment, along with a combination of germ and non-germ surface datasets. The training set, validation set, and test set were randomly divided by a ratio of 7:2:1. The experiment introduced the CBAM (Convolutional Block Attention Module) attention mechanism into MobileNetV2, improving the CBAM by replacing the cascade connection with a parallel connection, thus building an advanced mixed attention module, I_CBAM, and establishing a new model, I_CBAM_MobileNetV2. The proposed I_CBAM_MobileNetV2 achieved an accuracy of 98.21%, which was 4.88% higher than that of MobileNetV2. Compared to Xception, MobileNetV3, DenseNet121, E-AlexNet, and ResNet50, the accuracy was increased by 9.24%, 6.42%, 3.85%, 3.59%, and 2.57%, respectively. Gradient-Weighted Class Activation Mapping (Grad-CAM) network visualization demonstrates that I_CBAM_MobileNetV2 focuses more on distinguishing features in maize seed images, thereby boosting the accuracy of the model. Furthermore, the model is only 25.1 MB, making it suitable for portable deployment on mobile terminals. This study provides effective strategies and experimental methods for identifying maize seed varieties using deep learning technology. This research provides technical assistance for the non-destructive detection and automatic identification of maize seed varieties.

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 gliding arc (Glidarc) electric discharge generates nonthermal plasma at atmospheric pressure. This work proposes to investigate the effect of humid air Glidarc plasma on maize seeds by studying the early germination and the effect on maize pathogens potentially harmful. The seeds were treated for 300 and 900 s in spatial post-discharge mode. At the first sight, there was a coloration change of the surface of 900 s-treated seeds, which became darker than control ones. Scanning electron microscopy (SEM) analysis showed no change for 300-s-treated but significant changes for higher exposure (900 s) revealed by modification of the etched/eroded surface of the starch caryopsis resulting in water holes and large channels. The pathogenicity test through the fungal infection led to a degree of 58% for the untreated seeds and 6.7% and 13.3% for the seeds treated for 300 and 900 s, respectively, and, thus, an important load reduction of fungi when the seeds were pretreated. Then, germination percentage (GP) along the early growth parameters was evaluated and the results showed that under laboratory conditions, these parameters increased by 4.6%–17.4% for GP, 5.8%–36.7% for shoot length, and 1%–12.9% for root length. Furthermore, the Fourier transform infrared (FTIR) spectroscopy showed no change in the bonds between atoms after the plasma treatment but revealed a surface activation due to the degradation of lipids. This application could contribute to the fight against food insecurity in Cameroon and in the world without using chemical additives. [ABSTRACT FROM AUTHOR]

Maize is one of the essential crops for food supply. Accurate sorting of seeds is critical for cultivation and marketing purposes, while the traditional methods of variety identification are time-consuming, inefficient, and easily damaged. This study proposes a rapid classification method for maize seeds using a combination of machine vision and deep learning. 8080 maize seeds of five varieties were collected, and then the sample images were classified into training and validation sets in the proportion of 8:2, and the data were enhanced. The proposed improved network architecture, namely P-ResNet, was fine-tuned for transfer learning to recognize and categorize maize seeds, and then it compares the performance of the models. The results show that the overall classification accuracy was determined as 97.91, 96.44, 99.70, 97.84, 98.58, 97.13, 96.59, and 98.28% for AlexNet, VGGNet, P-ResNet, GoogLeNet, MobileNet, DenseNet, ShuffleNet, and EfficientNet, respectively. The highest classification accuracy result was obtained with P-ResNet, and the model loss remained at around 0.01. This model obtained the accuracy of classifications for BaoQiu, ShanCu, XinNuo, LiaoGe, and KouXian varieties, which reached 99.74, 99.68, 99.68, 99.61, and 99.80%, respectively. The experimental results demonstrated that the convolutional neural network model proposed enables the effective classification of maize seeds. It can provide a reference for identifying seeds of other crops and be applied to consumer use and the food industry.

The purpose of this study is the statistical analysis and discrimination of maize seed using a machine vision (MV) approach. The foundation of the digital image dataset holds six maize seed varieties named as Kargal K-9803, Gujjar Khan, Desi White, Pioner 30Y87, Syngenta ST-6142, and Pioner 31R88. The digital image dataset acquired via a digital imaging laboratory. For preprocessing, we crop the image into a size of 600×600 pixels, and convert it into a gray level image format. After that, line and edge detection are performed by using a Prewitt filter, and five non-overlapping areas of interest (AOIs) size of (200×200), and (250×250) are drawn. A total of 56 statistical features, containing texture features, histogram features, and spectral features, is extracted from each AOI. The 11 optimized statistical features have been selected by deploying "Correlation-based Feature Selection" (CFS) with the Greedy algorithm. For the discrimination analysis, four MV classifiers named as "Support Vector Machine" (SVM), "Logistic" (Lg), "Bagging" (B), and "LogitBoost" (LB) have been deployed on optimized statistical features dataset. After analysis, the SVM classifier has shown a promising accuracy of 99.93% on AOIs size (250×250). The obtained accuracy by SVM classifier on six maize seed varieties, namely Kargal K-9803, Gujjar Khan, Desi White, Pioner 30Y87, Syngenta ST-6142, and Pioner 31R88, were 99.9%, 99.8%, 100%, 100%, 99.9%, and 99.8%, respectively. [ABSTRACT FROM AUTHOR]

Unintended effects of genetically modified (GM) crops may pose safety issues. Omics techniques provide researchers with useful tools to assess such unintended effects. Proteomics and metabolomics analyses were performed for three GM maize varieties, 2A-7, CC-2, and 2A-7×CC-2 stacked transgenic maize, and the corresponding non-GM parent Zheng58. Proteomics revealed 120, 271 and 135 maize differentially expressed proteins (DEPs) in the 2A-7/Zheng58, CC-2/Zheng58 and 2A-7×CC-2/Zheng58 comparisons, respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that most DEPs participated in metabolic pathways and the biosynthesis of secondary metabolite. Metabolomics revealed 179, 135 and 131 differentially accumulated metabolites (DAMs) in the 2A-7/Zheng58, CC-2/Zheng58 and 2A-7×CC-2/Zheng58 comparisons, respectively. Based on KEGG enrichment analysis, most DAMs are involved in the biosynthesis of secondary metabolite and metabolic pathways. According to integrated proteomics and metabolomics analysis, the introduction of exogenous EPSPS did not affect the expression levels of six other enzymes or the abundance of seven metabolites involved in the shikimic acid pathway in CC-2 and 2A-7×CC-2 seeds. Six co-DEPs annotated by integrated proteomics and metabolomics pathway analysis were further analyzed by qRT-PCR. This study successfully employed integrated proteomic and metabolomic technology to assess unintended changes in maize varieties. The results suggest that GM and gene stacking do not cause significantly unintended effects. [ABSTRACT FROM AUTHOR]

Abstract Ethidium bromide (EB) and acridine orange (AO) bind to nucleic acids and are thus considered as potential mutagens. In this study, the effects of EB and AO on the germination behaviours of white, yellow, red, and purple maize seeds were investigated. The results indicate that low concentrations of EB (50 μg mL−1) and AO (500 μg mL−1) promote germination, particularly for the white and yellow seeds. However, high concentrations of EB (0.5 mg mL−1) and AO (5 mg mL−1) significantly inhibit germination, with the level of inhibition decreasing in the following order: white > yellow > red > purple. In addition, EB and AO induce H2O2 production in a concentration-dependent manner. The effects of these mutagens on seed germination were partly reversed by dimethyl thiourea, a scavenger of reactive oxygen species (ROS), and diphenylene iodonium (DPI), an inhibitor of NADPH oxidase, while the effects were enhanced by treatment with H2O2 and 3-amino-1,2,4-triazole, a specific inhibitor of catalase. In addition, AO and EB profoundly increased NADPH oxidase activities in germinating seeds. The treatment of seeds with EB and AO did not affect the growth or drought tolerance of the resultant seedlings. The findings suggest that the mechanism of mutagen toxicity is related to the induction of ROS production.

This article evaluated the pre-sowing treatment of seeds with electroozonation. A full factor experiment on the influence of the parameters of the technological process of electroozone treatment on the sowing qualities of maize seeds was carried out. Based on the experimental data, the effect of ozone concentration, treatment time and the time before sowing and after treatment on the germination ability, germination energy and growing energy of maize seeds was determined. According to the results of the study, the corresponding regression equations were calculated, graphical dependencies were constructed and method parameters for the pre-sowing treatment of maize seeds with ozone were determined.

The standard technology of seed processing uses mainly chemical products. Recent researches showed that toxic materials from chemical fertilizers can be harmful to humans, animals and the environment. Currently the attention of researches is shifting away from chemical fertlizers and toward alternative that consumers perceive to be natural, Plant Growth Promoting bacteria (PGP). PGP bacteria could be a way to reduce chemical fertilizer doses. This was the reason to test the ability of Bacillus megaterium, Azotobacter chroococcum to produce hormone auxin (IAA). Bacterial strains were identified by PCR amplification and sequencing of the 16S rRNA gene. Indole-3-acetic acid (IAA) was detected and quantified by MRM experiment. This study conducted that maize seed inoculation with IAA from species mentioned above showed positive effects. They had statistically significantly higher root and steam height compared to control seedlings. Bacterial strains tested in this study may be recommended as PGP (Plant Growth Promoting) bacteria, due to their positive effects and eventually can be used to reduce chemical fertilizers doses.

The purity of the seeds is one of the important factors that increase the yield. For this reason, the classification of maize cultivars constitutes a significant problem. Within the scope of this study, six different classification models were designed to solve this problem. A special dataset was created to be used in the models designed for the study. The dataset contains a total of 14,469 images in four classes. Images belong to four different maize types, BT6470, CALIPOS, ES_ARMANDI, and HIVA, taken from the BIOTEK company. AlexNet and ResNet50 architectures, with the transfer learning method, were used in the models created for the image classification. In order to improve the classification success, LSTM (Directional Long Short-Term Memory) and BiLSTM (Bi-directional Long Short-Term Memory) algorithms and AlexNet and ResNet50 architectures were hybridized. As a result of the classifications, the highest classification success was obtained from the ResNet50+BiLSTM model with 98.10%., Competing Interests: The authors declare that they have no conflict of interest., (© 2023 The Authors. Food Science & Nutrition published by Wiley Periodicals LLC.)

Streptomyces mycarofaciens SS-2-243 has been found to produce non-volatile compounds possessing antifungal activity against the growth of plant pathogens, but the effect of its volatile compounds remain unknown. Therefore, the efficacy of volatile compounds from S. mycarofaciens SS-2-243 (volatiles SS-2-243) grown on wheat seeds for 12 days was evaluated against four pathogenic fungi. In vitro studies using an antifungal bioassay tests on PDA dishes indicated that the volatiles SS-2-243 could totally suppress the growth of all four strains with most pronounced activity (100% inhibition) against Aspergillus parasiticus TISTR 3276 and A. flavus TISTR 3041. Identification of the volatiles SS-2-243 using gas chromatography–mass spectrometry (GC–MS) revealed 33 compounds, with the most abundant being 2-methylisoborneol. Effect of the inoculum size and spore concentration of S. mycarofaciens SS-2-243 prepared as a wheat seed inoculum on the suppression of the two aflatoxin producing fungi was studied. Complete growth inhibition (100%) was achieved at the optimum wheat seed inoculum size of at least 30 g L−1 and 107 spore mL−1. Use of 30 g L−1 wheat seed culture of S. mycarofaciens SS-2-243 could completely kill the conidia of A. parasiticus TISTR 3276 and A. flavus TISTR 3041 with 1 h and 3 h exposures, respectively. Based on the promising antifungal activity of the volatiles SS-2-243, fumigation with 30 g L−1 wheat seed culture of S. mycarofaciens SS-2-243 for 24 h completely controlled the growth of the two aflatoxin producing fungi infecting maize seeds, without adverse effects on maize seed germination. The main effect of the volatiles SS-2-243 was damage and complete inhibition of conidia germination as evident by scanning electron microscope (SEM) images. Therefore, this biofumigant has good potential to control the two aflatoxin producing fungi. [ABSTRACT FROM AUTHOR]

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Four fungal isolates were identified in this study of which three were Aspergillus species with Aspergillus flavus having the highest frequency followed by A. parasiticus. The result of high frequency of Aspergillus flavus and Aspergillus parasiticus in the Zea mays sample revealed production of aflatoxins. Maize sample in Awka was found to contain aflatoxin B1 (9.60ppb) and B2 (13.3ppb). Inhibition of A. flavus and A. parasiticus with Azadirachta indica and Garcinia kola seed extracts showed that the test plant extracts were effective for reducing mycelial growth on the test organism. Methanolic extract of G. kola showed antifungal inhibitory activity on the test organisms and the highest at 10% concentration. With ethanol extracts of G. kola, the antifungal activity was effective i.e. for inhibition of A. flavus and A. parasiticus, with A. parasiticus having the higher percentage inhibition at 10%. Inhibiting growth of Aspergillus flavus and Aspergillus parasiticus using methanolic and ethanolic extracts of neem seeds was effective in the inhibition of the test organism at 10%. The methanolic and ethanolic extracts of combined Garcinia kola and neem seeds revealed effective inhibition of A. flavus and A. parasiticus with ethanolic extracts of the combined test plants exerting the highest inhibition against A. flavus (80.43±3.62). The extracts from this plant show the ability to suppress growth of toxigenic A. flavus and A. parasiticus. Phytochemical analysis showed that the methanolic and ethanolic extracts of G. kola and neem seeds showed the presence of secondary metabolites and this may be a reason for the inhibitory activity on A. flavus and A. parasiticus. Results from this study will be important in planning a management strategy against aflatoxin-producing fungi and other fungi associated with spoilage of stored food products. [ABSTRACT FROM AUTHOR]

Smallholder farmers play a major role in crop production towards household food security, particularly in resource-poor communities. Maize is a common crop produced in smallholder farming and it is cultivated from seeds that has been stored and re-used for years. Spoilage of stored grains is a major challenge, which leads to yield loss and poor seed quality. The objectives of this study were to evaluate in vivo antifungal activity of selected plant extracts against Fusarium pathogens on maize seeds, and to evaluate their phytotoxicity on seed germination and seedling growth. Fresh leaves collected from eight medicinal plants were dried and selectively extracted with water, ethyl acetate or acetone. The dried extracts were evaluated for antifungal activity against Fusarium pathogens (F. proliferatum, F. oxysporum, F. subglutinans, F. verticilloides, F. semitectum, F. chlamydosporum, F. solani, F. equisite and F. graminearum) inoculated on maize seeds. Melia azedarach acetone extract showed strong antifungal activity (97% inhibition) against F. proliferatum while combined acetone extracts from Combretum erythrophyllum and Quercus acutissima exhibited 96%, 67% and 56% inhibition against F. verticilloides, F. proliferatum and F. solani, respectively. With the exception of Quercus acutissima ethyl acetate, none of the extracts significantly inhibited seed germination when compared to untreated seeds. This study showed that plant extracts could control Fusarium diseases without any adverse effects on maize seed germination or plant growth.

• An online NIR system was developed to collect single seed spectrum. • A dynamic white reference acquisition method was developed. • The system was evaluated for protein and insect infestation detection. Maize is one of the most important crops in the world. The protein content and insect infestation significantly influence seed vigor and growth. Therefore, it is vital to detect the protein content and insect infestation seeds rapidly and non-destructively. In this study, a near-infrared (NIR) spectra acquisition device (900–1700 nm) was designed and employed for on-line seed quality detection. Support vector machine (SVM), logistic regression (LR), and partial least square regression discrimination analysis (PLS-DA) were used for insect infestation seeds classification. PLS and least squares-support vector machine (LS-SVM) were adopted for protein detection. Competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were applied to select the feature wavelengths to reduce the redundant data and identify important information. As for insect infestation seeds, CARS-SPA-LR model achieved the classification using only 7 features with an accuracy of 0.83. In terms of protein prediction, the LS-SVM models obtained the best results for grain protein content (GPC, %) and absolute GPC (Ab_GPC, mg/kernel), respectively. The optimal models only used 22 and 21 feature wavelengths selected by CARS-SPA, with the RMSEP of 3.38 % and 2.38 mg/kernel, and RPD was 2.08 and 2.11, respectively. The results indicated that the NIR on-line acquisition system could be applied for qualitative and quantitative analysis of maize seeds. [ABSTRACT FROM AUTHOR]

Abstract: To explore the relation of maize grain texture and phenotypic traits with grain thin-layer drying rate, we observed the ultra-structure of maize grain, and tested three traits about the maize grain texture and four phenotypic traits. The vitreous part percentage was different (P

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., Competing Interests: The research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Fan, An, Wang, Yang, Huang, Wang, Zhao and Tian.)