X-ray CT image analysis for morphology of muskmelon seed in relation to germination

Ahmed, Mohammed Raju; Yasmin, Jannat; Wakholi, Collins; Cho, Byoung‐Kwan

doi:10.1016/j.biosystemseng.2018.09.015

Cited by 43 publications

(22 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These results are consistent with those observed in bean (SOOD et al, 2016) and melon seeds (AHMED et al, 2018), confirming the efficiency of the X-ray technique to evaluate mechanical damage to seeds. Also, the seed energy reserves are directed to the embryonic axis during the process of germination and establishment of seedlings (FINCH-SAVAGE; BASSEL, 2016), then it was expected that damaged seeds would generate abnormal seedling.…”

Section: Resultssupporting

confidence: 89%

Non-destructive identification of physical damage in mung bean (Vigna radiata L.) seeds by X-ray image analysis

et al. 2020

View full text Add to dashboard Cite

Global demand for pulses such as the mung bean has grown in the last years. For successful production of these crops it is necessary to use high quality seeds. Methodologies based on X-ray image analysis have been used as a complementary tool to evaluate the physical quality of seeds due to their speed and potential for automation. The aim of this study was to evaluate the efficiency of X-ray analysis for non-destructive evaluation of the physical quality of Vigna radiata seeds and to relate the variables obtained with their physiological potential. For this, seeds from eight lots were X-rayed and subsequently subject to germination test. In total, 18 physical and physiological parameters were determined. The X-ray image analysis was efficient for evaluating the internal morphology of Vigna radiata seeds and allowed the identification of various damage types. However, it was not possible to relate the physical variables to the seed quality as the lots presented similar germination percentage. Physical variables such as solidity and circularity are related to percentage of root protrusion and length of seedling hypocotyl. Low relative densities indicate deteriorated tissues, related to severe morphological damage and non-viable seeds.

show abstract

Section: Resultssupporting

confidence: 89%

Non-destructive identification of physical damage in mung bean (Vigna radiata L.) seeds by X-ray image analysis

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In the seed industry, quality assurance programs rely on numerous methods to certify seed quality attributes, such as germination and vigor tests [2]. These procedures have limitations related to time consumption, subjectivity, and the destructive nature of assessing seed quality [3][4][5]. In fact, there is a growing demand for efficient methods that can provide a quick, reliable, non-destructive, and objective detection of seed quality [6].…”

Section: Introductionmentioning

confidence: 99%

“…Usually, changes in chemical composition and internal anatomical characteristics of seeds are correlated with loss of viability and vigor [5], but these changes are unlikely to be identified by visual inspection. Meanwhile, methods based on spectrometry and X-ray imaging techniques have been successfully used to collect data on complex traits related to seed quality.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, X-ray imaging is based on differences in X-ray attenuation in different types of tissues [18]. Hence, it can reveal the physical state of the seed, i.e., its internal morphology [5]. Although these techniques have high potential for seed quality classification, combining datasets may generate new information about seed samples or improve the performance of classifiers [19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning for Seed Quality Classification: An Advanced Approach Using Merger Data from FT-NIR Spectroscopy and X-ray Imaging

Medeiros

Silva

Ribeiro

et al. 2020

Sensors

View full text Add to dashboard Cite

Optical sensors combined with machine learning algorithms have led to significant advances in seed science. These advances have facilitated the development of robust approaches, providing decision-making support in the seed industry related to the marketing of seed lots. In this study, a novel approach for seed quality classification is presented. We developed classifier models using Fourier transform near-infrared (FT-NIR) spectroscopy and X-ray imaging techniques to predict seed germination and vigor. A forage grass (Urochloa brizantha) was used as a model species. FT-NIR spectroscopy data and radiographic images were obtained from individual seeds, and the models were created based on the following algorithms: linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), random forest (RF), naive Bayes (NB), and support vector machine with radial basis (SVM-r) kernel. In the germination prediction, the models individually reached an accuracy of 82% using FT-NIR data, and 90% using X-ray data. For seed vigor, the models achieved 61% and 68% accuracy using FT-NIR and X-ray data, respectively. Combining the FT-NIR and X-ray data, the performance of the classification model reached an accuracy of 85% to predict germination, and 62% for seed vigor. Overall, the models developed using both NIR spectra and X-ray imaging data in machine learning algorithms are efficient in quickly, non-destructively, and accurately identifying the capacity of seed to germinate. The use of X-ray data and the LDA algorithm showed great potential to be used as a viable alternative to assist in the quality classification of U. brizantha seeds.

show abstract

“…In order to achieve accurate monitoring and quality control, reliable and nondestructive testing methods are needed. Recently, nondestructive methods, such as X-ray diffraction [ 11 , 12 ], laser speckle analysis techniques [ 13 , 14 ], and measurement of electrical conductivity [ 15 , 16 ], were proposed for vigor detection. As a result of the low efficiency and complicated operation, the applications for seed detection based on these methods were limited [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Sweet Corn Seed Germination Based on Hyperspectral Image Technology and Multivariate Data Regression

Cui

Cheng

et al. 2020

Sensors

View full text Add to dashboard Cite

Vigor identification in sweet corn seeds is important for seed germination, crop yield, and quality. In this study, hyperspectral image (HSI) technology integrated with germination tests was applied for feature association analysis and germination performance prediction of sweet corn seeds. In this study, 89 sweet corn seeds (73 for training and the other 16 for testing) were studied and hyperspectral imaging at the spectral range of 400–1000 nm was applied as a nondestructive and accurate technique to identify seed vigor. The root length and seedling length which represent the seed vigor were measured, and principal component regression (PCR), partial least squares (PLS), and kernel principal component regression (KPCR) were used to establish the regression relationship between the hyperspectral feature of seeds and the germination results. Specifically, the relevant characteristic band associated with seed vigor based on the highest correlation coefficient (HCC) was constructed for optimal wavelength selection. The hyperspectral data features were selected by genetic algorithm (GA), successive projections algorithm (SPA), and HCC. The results indicated that the hyperspectral data features obtained based on the HCC method have better prediction results on the seedling length and root length than SPA and GA. By comparing the regression results of KPCR, PCR, and PLS, it can be concluded that the hyperspectral method can predict the root length with a correlation coefficient of 0.7805. The prediction results of different feature selection and regression algorithms for the seedling length were up to 0.6074. The results indicated that, based on hyperspectral technology, the prediction of seedling root length was better than that of seed length.

show abstract

X-ray CT image analysis for morphology of muskmelon seed in relation to germination

Cited by 43 publications

References 31 publications

Non-destructive identification of physical damage in mung bean (Vigna radiata L.) seeds by X-ray image analysis

Non-destructive identification of physical damage in mung bean (Vigna radiata L.) seeds by X-ray image analysis

Machine Learning for Seed Quality Classification: An Advanced Approach Using Merger Data from FT-NIR Spectroscopy and X-ray Imaging

Prediction of Sweet Corn Seed Germination Based on Hyperspectral Image Technology and Multivariate Data Regression

Contact Info

Product

Resources

About