| Title |
Machine vision based alternative testing approach for physical purity, viability and vigour testing of soybean seeds (Glycine max)
|
|---|---|
| Published in |
Journal of Food Science and Technology, July 2018
|
| DOI | 10.1007/s13197-018-3320-x |
| Pubmed ID | |
| Authors |
Shveta Mahajan, Sudesh Kumar Mittal, Amitava Das |
| Abstract |
The conventional methods for seed quality testing have several limitations as they involve visual assessment and are destructive. In this context, a study was performed to assess the suitability of non-contact, non-destructive type imaging techniques such as visible imaging and X-ray imaging for conducting physical purity, viability and vigour tests of soybean seeds. The seeds that appeared healthy in external surface examination using visible tests as well as in internal assessment using X-ray tests were classified as sound seeds while the other seeds were marked as not-sound seeds. The obtained results were then correlated with the results of the standard germination tests. The high correlation results between the imaging tests and the standard conventional germination tests indicate the effectiveness and usability of the proposed image analysis based technique as an attractive alternative to the existing quality assessment methods for soybean seeds. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 51 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 16% |
| Student > Ph. D. Student | 7 | 14% |
| Student > Bachelor | 5 | 10% |
| Researcher | 3 | 6% |
| Student > Doctoral Student | 1 | 2% |
| Other | 5 | 10% |
| Unknown | 22 | 43% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 13 | 25% |
| Engineering | 8 | 16% |
| Computer Science | 4 | 8% |
| Chemistry | 1 | 2% |
| Unspecified | 1 | 2% |
| Other | 0 | 0% |
| Unknown | 24 | 47% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 September 2018.
All research outputs
#20,533,782
of 23,103,903 outputs
Outputs from Journal of Food Science and Technology
#1,111
of 1,455 outputs
Outputs of similar age
#286,161
of 326,641 outputs
Outputs of similar age from Journal of Food Science and Technology
#28
of 50 outputs
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