Rapid and non-destructive detection method for water status and water distribution of rice seeds with different vigor

Song, Peng; Kim, Ghiseok; Peng, Song; Yang, Tao; Yue, Xia; Gu, Ying

doi:10.25165/j.ijabe.20211402.5780

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…Low-field nuclear magnetic resonance (LF-NMR) technology can test samples rapidly, accurately, and non-destructively [22][23][24] , and can analyze the internal water phase, distribution characteristics, and flow characteristics of samples at a submicroscopic level [25][26][27] . Thus, it can reveal the internal water state and patterns of change during the germination of crop seeds [28,29] .…”

Section: Introductionmentioning

confidence: 99%

Determining the effects of LFHV-PEF treatment on the water distribution and vigor of aged rice seeds using LF-NMR

Cheng

Song

Hou

et al. 2022

International Journal of Agricultural and Biological Engineering

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This study aimed to investigate the effect of low-frequency high-voltage pulsed electric field (LFHV-PEF) treatment on the germination of aged rice seeds. Aged rice seeds were subjected to LFHV-PEF treatment with different electric field strengths, and low-field nuclear magnetic resonance (LF-NMR) was performed to acquire the LF-NMR data of rice seeds at different germination periods during a standard seed germination test to analyze their internal patterns of water state and distribution. Optimal treatment conditions were determined based on the physicochemical data collected during germination, and the improvements in seed vigor were verified. The findings indicated that during germination, the contents of bound and semi-bound water within the aged rice seeds initially increased and then decreased, whereas free water and total water contents increased continuously and rapidly. Side peaks were also observed within the seeds. Under the LFHV-PEF treatment, the semi-bound water within the seeds was more easily converted to free water, and the water absorption rate, germination potential, germination rate, germination index, and vigor index of these seeds improved. Further, the optimal electrical field strength was 12 kV. By analyzing the internal patterns of water state and distribution in seeds, the mechanism by which electric field treatment improved seed vigor was elucidated, thus, providing theoretical support and data evidence for research on water absorption during the germination of rice seeds, and methods for improving seed vigor.

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Section: Introductionmentioning

confidence: 99%

Determining the effects of LFHV-PEF treatment on the water distribution and vigor of aged rice seeds using LF-NMR

Cheng

Song

Hou

et al. 2022

International Journal of Agricultural and Biological Engineering

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“…The traditional methods of evaluation of seed germination and aging are based on color and aroma indicators or through standard laboratory tests such as standard germination, accelerated aging test, electrical conductivity test, seedling growth test, cold test, and tetrazolium [8,9]. Despite their high precision, these methods are not practical on a commercial scale as they are usually nonautomatic, time-consuming, and destructive and/or require specialized training and experience [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Application of Convolutional Neural Network Coupled with Hyperspectral Imaging to Determine the Viability of Peanut Seeds and Its Comparison with Conventional Machine Learning Methods Based on UV-Vis Spectroscopy

Rajabi-Sarkhani,

Gilandeh,

Moinfar

et al. 2023

Preprint

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Seeds can maintain their quality for a limited time; after that, they will lose their germination ability and vigor. Some physiological and physicochemical changes in the structure of the seeds during storage can decrease the quality of the seeds which is known as aging. Therefore, detection of the strong young seeds from the old ones is a vital issue in the modern agriculture. Conventional methods of detection of the seed viability and germination are destructive, time-consuming and costly. In this research, two peanut cultivars, namely North Carolina 2 (NC-2) and Florispan were selected and three artificial aging levels were induced to them. Hyperspectral images (HSI) of the samples were acquired and the seed viability was evaluated using two pre-trained convolutional neural network (CNN) image processing models, AlexNet and VGGNet. The noise of the reflection spectra of the samples was relatively resolved and modified by combining Preprocessing techniques of moving average (MA) and standard normal variate (SNV). Using principal component analysis (PCA), the dimensions were declined and three principal components (PC) were extracted. These PCs were then used as variables in the classification of support vector machine (SVM) and linear discriminant analysis (LDA). The results showed the high capability of CNN architectures such as AlexNet and VGGNet in detection of the seed viability based on the HIS with no pre-processing and feature extraction. The mentioned architectures reached the accuracy of 0.985 and 0.986, respectively. The combination of feature extraction method of PCA with LDA and SVM classifiers showed that the use of a limited number of PCs instead of all wavelengths can decrease the complexity of modeling, while enhancing the efficiency of the models such that LDA and SVM classifiers achieved the accuracy of 0.983 and 0.986 in classification of peanut sees, respectively.

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“…Therefore, it is of great significance to distinguish aged seeds for ensuring seed quality and reducing economic losses. The traditional inspection of aged seeds is based on the indicators of color and aroma, germination test, tetrazole staining [ 4 , 5 , 6 ], etc. However, all these methods are time-consuming, and the seed cannot maintain its original state, including when being discarded [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis

Wang

Zhang

Song

et al. 2021

Sensors

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Seed aging detection and viable seed prediction are of great significance in alfalfa seed production, but traditional methods are disposable and destructive. Therefore, the establishment of a rapid and non-destructive seed screening method is necessary in seed industry and research. In this study, we used multispectral imaging technology to collect morphological features and spectral traits of aging alfalfa seeds with different storage years. Then, we employed five multivariate analysis methods, i.e., principal component analysis (PCA), linear discrimination analysis (LDA), support vector machines (SVM), random forest (RF) and normalized canonical discriminant analysis (nCDA) to predict aged and viable seeds. The results revealed that the mean light reflectance was significantly different at 450~690 nm between non-aged and aged seeds. LDA model held high accuracy (99.8~100.0%) in distinguishing aged seeds from non-aged seeds, higher than those of SVM (87.4~99.3%) and RF (84.6~99.3%). Furthermore, dead seeds could be distinguished from the aged seeds, with accuracies of 69.7%, 72.0% and 97.6% in RF, SVM and LDA, respectively. The accuracy of nCDA in predicting the germination of aged seeds ranged from 75.0% to 100.0%. In summary, we described a nondestructive, rapid and high-throughput approach to screen aged seeds with various viabilities in alfalfa.

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Rapid and non-destructive detection method for water status and water distribution of rice seeds with different vigor

Cited by 5 publications

References 25 publications

Determining the effects of LFHV-PEF treatment on the water distribution and vigor of aged rice seeds using LF-NMR

Determining the effects of LFHV-PEF treatment on the water distribution and vigor of aged rice seeds using LF-NMR

Application of Convolutional Neural Network Coupled with Hyperspectral Imaging to Determine the Viability of Peanut Seeds and Its Comparison with Conventional Machine Learning Methods Based on UV-Vis Spectroscopy

Non-Destructive Identification of Naturally Aged Alfalfa Seeds via Multispectral Imaging Analysis

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