scholarly journals X-ray imaging and digital processing application in non-destructive assessing of melon seed quality

2020 ◽  
Vol 42 ◽  
Author(s):  
André Dantas de Medeiros ◽  
Maycon Silva Martins ◽  
Laércio Junio da Silva ◽  
Márcio Dias Pereira ◽  
Manuel Jesús Zavala León ◽  
...  
Keyword(s):  
Large Scale ◽  
Seed Quality ◽  
Automated Analysis ◽  
Digital Processing ◽  
Radiographic Images ◽  
Processing Application ◽  
X Ray ◽  
Digital Radiographs ◽  
Melon Seeds ◽  
Non Destructive

Abstract: Non-destructive and high throughput methods have been developed for seed quality evaluation. The aim of this study was to relate parameters obtained from the free and automated analysis of digital radiographs of hybrid melons’ seeds to their seeds’ physiological potential. Seeds of three hybrid melon (Cucumis melo L.) cultivars from commercial lot samples were used. Radiographic images of the seeds were obtained, from which area, perimeter, circularity, relative density, integrated density and seed filling measurements were generated by means of a macro (PhenoXray) developed for ImageJ® software. After the X-ray test, seed samples were submitted to the germination test, from which variables related to the physiological quality of the seeds were obtained. Variability between lots was observed for both physical and physiological characteristics. Results showed that the use of the PhenoXray macro allows large-scale phenotyping of seed radiographs in a simple, fast, consistent and completely free way. The methodology is efficient in obtaining morphometric and tissue integrity data of melon seeds and the generated parameters are closely related to physiological attributes of seed quality.

scholarly journals Parameters based on X-ray images to assess the physical and physiological quality of Leucaena leucocephala seeds

2018 ◽  
Vol 42 (6) ◽  
pp. 643-652 ◽  
Author(s):  
André Dantas de Medeiros ◽  
Joyce de Oliveira Araújo ◽  
Manuel Jesús Zavala León ◽  
Laércio Junio da Silva ◽  
Denise Cunha Fernandes dos Santos Dias
Keyword(s):  
Leucaena Leucocephala ◽  
High Performance ◽  
Principal Component ◽  
Automated Analysis ◽  
Radiographic Images ◽  
X Ray ◽  
Physiological Quality ◽  
Non Destructive ◽  
Do So

ABSTRACT Non-destructive and high performance analyses are highly desirable and important for assessing the quality of forest seeds. The aim of this study was to relate parameters obtained from semi-automated analysis of radiographs of Leucaena leucocephala seeds to their physiological potential by means of multivariate analysis. To do so, seeds from five lots collected from parent trees from the region of Viçosa, MG, Brazil, were used. The study was carried out through analysis of radiographic images of seeds, from which the percentage of damaged seeds (predation and fungi), and measurements of area, perimeter, circularity, relative density, and integrated density of the seeds were obtained. After the X-ray test, the seeds were tested for germination in order to assess variables related to seed physiological quality. Multivariate statistics were applied to the data generated, with use of principal component analysis (PCA). X-ray testing allowed visualization of details of the internal structure of seeds and differences regarding density of seed tissues. Semi-automated analysis of radiographic images of Leucaena leucocephala seeds provides information on seed physical characteristics and generates parameters related to seed physiological quality in a simple, fast, and inexpensive manner.

scholarly journals Use of radiographic images for rapid and non-destructive assessment of crambe seed quality

2021 ◽  
Vol 43 ◽  
Author(s):  
Alessandra da Silva Ribeiro ◽  
Tássia Fernanda Santos Neri ◽  
André Dantas de Medeiros ◽  
Carla do Carmo Milagres ◽  
Laércio Junio da Silva
Keyword(s):  
Seed Quality ◽  
Automated Analysis ◽  
Physical Parameters ◽  
Morphometric Characteristics ◽  
Radiographic Images ◽  
X Ray ◽  
Tissue Integrity ◽  
Seed Embryo ◽  
Physiological Quality

Abstract: Technologies based on electromagnetic radiation, such as the X-ray technique, has contributed to the establishment of new and promising methodologies for evaluating seed quality. This study aimed to relate parameters based on semi-automated analysis of radiographs of crambe seeds to their physiological quality. Radiographic images of seeds from 10 seed lots of cultivar FMS Brilhante were semi-automatically analyzed using ImageJ® software. Measurements of morphometric characteristics and tissue integrity were obtained for the seeds, as well as individually for the seed embryo. Following X-ray test, the seeds were subject to germination and seedling growth test. It was possible to visualize the internal structures of the seeds in the radiographs. There were differences in the physical parameters obtained by the semi-automated analysis of the radiographs between the seed lots. Also, the lots differed regarding the physiological quality of the seeds. Morphometric characteristics and tissue integrity, especially for the seed embryo, showed high correlation with the seed physiological quality. Therefore, this work presents an efficient approach to rapid and non-destructively assess the quality of crambe seeds.

scholarly journals Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality

2020 ◽  
Author(s):  
Vitor J Bianchini ◽  
Gabriel M Mascarin ◽  
Lúcia CAS Silva ◽  
Valter Arthur ◽  
Jean M Carstensen ◽  
...  
Keyword(s):  
Jatropha Curcas ◽  
Seed Quality ◽  
Alternative Methods ◽  
Quality Analysis ◽  
Classification Models ◽  
X Ray ◽  
Imaging Technologies ◽  
X Ray Imaging ◽  
Non Destructive

Abstract Background: The use of non-destructive methods with less human interference is of great interest in agricultural industry and crop breeding. Modern imaging technologies enable the automatic visualization of multi-parameter for characterization of biological samples, reducing subjectivity and optimizing the analysis process. Furthermore, the combination of two or more imaging techniques has contributed to discovering new physicochemical tools and interpreting datasets in real time.Results: We present a new method for automatic characterization of seed quality based on the combination of multispectral and X-ray imaging technologies. We proposed an approach using X-ray images to investigate internal tissues because seed surface profile can be negatively affected, but without reaching important internal regions of seeds. An oilseed plant (Jatropha curcas) was used as a model species, which also serve as a multi-purposed crop of economic importance worldwide. Our studies included the application of a normalized canonical discriminant analyses (nCDA) algorithm as a supervised transformation building method to obtain spatial and spectral patterns on different seedlots. We developed classification models using reflectance data and X-ray classes based on linear discriminant analysis (LDA). The classification models, individually or combined, showed high accuracy (>0.96) using reflectance at 940 nm and X-ray data to predict quality traits such as normal seedlings, abnormal seedlings and dead seeds. Conclusions: Multispectral and X-ray imaging have a strong relationship with seed physiological performance. Reflectance at 940 nm and X-ray data can efficiently predict seed quality attributes. These techniques can be alternative methods for rapid, efficient, sustainable and non-destructive characterization of seed quality in the future, overcoming the intrinsic subjectivity of the conventional seed quality analysis.

scholarly journals Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality

2020 ◽  
Author(s):  
Vitor J Bianchini ◽  
Gabriel M Mascarin ◽  
Lúcia CAS Silva ◽  
Valter Arthur ◽  
Jean M Carstensen ◽  
...  
Keyword(s):  
Jatropha Curcas ◽  
Seed Quality ◽  
Alternative Methods ◽  
Quality Analysis ◽  
Classification Models ◽  
X Ray ◽  
Imaging Technologies ◽  
X Ray Imaging ◽  
Non Destructive

Abstract Background: The use of non-destructive methods with less human interference is of great interest in agricultural industry and crop breeding. Modern imaging technologies enable the automatic visualization of multi-parameter for characterization of biological samples, reducing subjectivity and optimizing the analysis process. Furthermore, the combination of two or more imaging techniques has contributed to discovering new physicochemical tools and interpreting datasets in real time.Results: We present a new method for automatic characterization of seed quality based on the combination of multispectral and X-ray imaging technologies. We proposed an approach using X-ray images to investigate internal tissues because seed surface profile can be negatively affected, but without reaching important internal regions of seeds. An oilseed plant (Jatropha curcas) was used as a model species, which also serve as a multi-purposed crop of economic importance worldwide. Our studies included the application of a normalized canonical discriminant analyses (nCDA) algorithm as a supervised transformation building method to obtain spatial and spectral patterns on different seedlots. We developed classification models using reflectance data and X-ray classes based on linear discriminant analysis (LDA). The classification models, individually or combined, showed high accuracy (>0.96) using reflectance at 940 nm and X-ray data to predict quality traits such as normal seedlings, abnormal seedlings and dead seeds.Conclusions: Multispectral and X-ray imaging have a strong relationship with seed physiological performance. Reflectance at 940 nm and X-ray data can efficiently predict seed quality attributes. These techniques can be alternative methods for rapid, efficient, sustainable and non-destructive characterization of seed quality in the future, overcoming the intrinsic subjectivity of the conventional seed quality analysis.

scholarly journals Radiographic analysis for the evaluation of polyembryony in Swingle citrumelo seeds

2018 ◽  
Vol 40 (2) ◽  
pp. 118-126
Author(s):  
Natália Arruda ◽  
Silvio Moure Cicero ◽  
Francisco Guilhien Gomes Junior
Keyword(s):  
Indirect Method ◽  
Seed Quality ◽  
Direct Method ◽  
Automated Systems ◽  
Radiographic Image ◽  
X Ray ◽  
Internal Morphology ◽  
Non Destructive ◽  
Swingle Citrumelo ◽  
Precision Test

Abstract: The polyembryony rate is a very important factor to consider when choosing a commercial rootstock. Currently, automated systems are used to improve seed quality analyses. X-ray testing is a fast, simple, non-destructive, high-precision test that allows to examine in detail the internal morphology of the seeds to identify damaged areas, their location and types of damage. In this context, the present research aimed to verify the possibility of using X-ray test to evaluate the polyembryony in Swingle citrumelo seeds. Seeds from seven lots were submitted to X-ray tests, direct method (embryo counts) and indirect method (germination). According to the results obtained, it was observed that there was a high coincidence between the number of embryos per seed analyzed using X-ray test and the direct method. Radiographic image analysis is efficient to evaluate the polyembryony in seeds of Swingle citrumelo.

Data augmentation by CycleGAN-based extra-supervised for non-destructive testing

2021 ◽  
Author(s):  
Jiangshan Ai ◽  
Lulu Tian ◽  
Libing Bai ◽  
Jie Zhang
Keyword(s):  
High Performance ◽  
Field Data ◽  
Large Scale ◽  
Data Augmentation ◽  
Learning Method ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
Non Destructive

Abstract Deep learning method is widely used in computer vision tasks with large scale annotated datasets. However, it is a big challenge to obtain such datasets in most directions of the vision based non-destructive testing (NDT) field. Data augmentation is proved as an efficient way in dealing with the lack of large-scale annotated datasets. In this paper, we propose CycleGAN-based extra-supervised (CycleGAN-ES) to generate synthetic NDT images, where the ES is used to ensure that the bidirectional mapping are learned for corresponding label and defect. Furthermore, we show the effectiveness of using the synthesized images to train deep convolutional neural networks (DCNN) for defects recognition. In the experiments, we extract numbers of X-ray welding images with both defect and no-defect from the published GDXray dataset, CycleGAN-ES are used to generate the synthetic defect images based on a small number of extracted defect images and manually drawn labels which are used as a content guide. For quality verification of the synthesized defect images, we use a high-performance classifier pre-trained using big dataset to recognize the synthetic defects and show comparability of the performances of classifiers trained using synthetic defects and real defects respectively. To present the effectiveness of using the synthesized defects as an augmentation method, we train and evaluate the performances of DCNN for defects recognition with or without the synthesized defects.

Quick and accurate selection of hand images among radiographs from various body parts using deep learning

2020 ◽  
Vol 28 (6) ◽  
pp. 1199-1206
Author(s):  
Kohei Fujiwara ◽  
Wanxuan Fang ◽  
Taichi Okino ◽  
Kenneth Sutherland ◽  
Akira Furusaki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Body Parts ◽  
Scoring Method ◽  
Radiographic Images ◽  
X Ray ◽  
Training Images ◽  
Picture Archiving And Communication ◽  
Fully Connected ◽  
Selection Of

BACKGROUND: Although rheumatoid arthritis (RA) causes destruction of articular cartilage, early treatment significantly improves symptoms and delays progression. It is important to detect subtle damage for an early diagnosis. Recent software programs are comparable with the conventional human scoring method regarding detectability of the radiographic progression of RA. Thus, automatic and accurate selection of relevant images (e.g. hand images) among radiographic images of various body parts is necessary for serial analysis on a large scale. OBJECTIVE: In this study we examined whether deep learning can select target images from a large number of stored images retrieved from a picture archiving and communication system (PACS) including miscellaneous body parts of patients. METHODS: We selected 1,047 X-ray images including various body parts and divided them into two groups: 841 images for training and 206 images for testing. The training images were augmented and used to train a convolutional neural network (CNN) consisting of 4 convolution layers, 2 pooling layers and 2 fully connected layers. After training, we created software to classify the test images and examined the accuracy. RESULTS: The image extraction accuracy was 0.952 and 0.979 for unilateral hand and both hands, respectively. In addition, all 206 test images were perfectly classified into unilateral hand, both hands, and the others. CONCLUSIONS: Deep learning showed promise to enable efficiently automatic selection of target X-ray images of RA patients.

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

2020 ◽  
Vol 36 (3) ◽  
Author(s):  
Thiago Pulici Martins Machado ◽  
André Dantas de Medeiros ◽  
Daniel Teixeira Pinheiro ◽  
Laércio Junio Da Silva ◽  
Denise Cunha Fernandes dos Santos Dias
Keyword(s):  
Image Analysis ◽  
Vigna Radiata ◽  
Mung Bean ◽  
Seed Quality ◽  
Physical Damage ◽  
Physical Quality ◽  
X Ray ◽  
Physical Variables ◽  
Non Destructive

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.

scholarly journals Radiographic Visibility Limit of Pores in Metal Powder for Additive Manufacturing

Metals ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1634
Author(s):  
Gerd-Rüdiger Jaenisch ◽  
Uwe Ewert ◽  
Anja Waske ◽  
Alexander Funk
Keyword(s):  
Digital Radiography ◽  
Measurement Techniques ◽  
Feedstock Powder ◽  
X Ray ◽  
Metallic Particles ◽  
Digital Radiographs ◽  
Quality Control Tool ◽  
X Ray Computed ◽  
Destructive Measurement ◽  
Non Destructive

The quality of additively manufactured (AM) parts is determined by the applied process parameters used and the properties of the feedstock powder. The influence of inner gas pores in feedstock particles on the final AM product is a phenomenon which is difficult to investigate since very few non-destructive measurement techniques are accurate enough to resolve the micropores. 3D X-ray computed tomography (XCT) is increasingly applied during the process chain of AM parts as a non-destructive monitoring and quality control tool and it is able to detect most of the pores. However, XCT is time-consuming and limited to small amounts of feedstock powder, typically a few milligrams. The aim of the presented approach is to investigate digital radiography of AM feedstock particles as a simple and fast quality check with high throughput. 2D digital radiographs were simulated in order to predict the visibility of pores inside metallic particles for different pore and particle diameters. An experimental validation was performed. It was demonstrated numerically and experimentally that typical gas pores above a certain size (here: 3 to 4.4 µm for the selected X-ray setup), which could be found in metallic microparticles, were reliably detected by digital radiography.

scholarly journals Description of a pure seed fraction of oat through usual evaluations and radiographic images

2002 ◽  
Vol 37 (8) ◽  
pp. 1183-1188 ◽  
Author(s):  
Roque Mario Craviotto ◽  
Ana Maria Yoldjian ◽  
Adriana Rita Salinas ◽  
Miriam Raquel Arango ◽  
Vilma Bisaro ◽  
...  
Keyword(s):  
Avena Sativa ◽  
Seed Quality ◽  
Experimental Equipment ◽  
Radiographic Images ◽  
X Ray ◽  
Single Seed ◽  
Partition Method ◽  
Precision Balance

The objective of this work was to evaluate the width and length incidence in a single seed fraction of oat [Avena sativa (L.)] cv. Cristal. The seeds were selected by a mechanical divider and by hand, and their correspondence to radiographic images in seeds with glumes and their caryopses. The width and length of the seeds with glumes and their caryopses were measured with electronic calliper, and their weight, with precision balance. Radiographic images of seeds with glumes were taken with an X-ray experimental equipment. The analyst selected seeds with glumes by the width and by the length previously determined and so with more weight, than that obtained by hand selection was slightly narrower, larger and lighter. The presence of the glumes masked the caryopses real dimensions (width and length), and conduced the analyst to select seeds that differed more by the width than by the length. The radiographic images showed the presence, or not, of caryopses inside the seed and its real dimensions. The mechanical partition method for seeds showed to be more efficient because the analyst subjectivity was not considered when the selection upon its dimensions was done. The X-ray analysis was a useful tool that complements the pure seed fraction selection as another factor of seed quality.

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