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Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/76756
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Shekh Mukhtar Mansuri | en_US |
dc.contributor.author | Subir Kumar Chakraborty | en_US |
dc.contributor.author | Naveen Kumar Mahanti | en_US |
dc.contributor.author | R Pandiselvam | en_US |
dc.date.accessioned | 2023-04-07T06:20:53Z | - |
dc.date.available | 2023-04-07T06:20:53Z | - |
dc.date.issued | 2022-09-01 | - |
dc.identifier.citation | Mansuri, S. M., Chakraborty, S. K., Mahanti, N. K., & Pandiselvam, R. (2022). Effect of germ orientation during Vis-NIR hyperspectral imaging for the detection of fungal contamination in maize kernel using PLS-DA, ANN and 1D-CNN modelling. Food Control, 139, 109077. | en_US |
dc.identifier.issn | Not Available | - |
dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/76756 | - |
dc.description | Not Available | en_US |
dc.description.abstract | Fungal contamination of maize during pre and post-harvest is rampant and omnipresent. Hyperspectral imaging (HSI) is a popular non-invasive technique for detection of fungal contamination in maize kernels and secures acceptance for online implementation. Designed experiments were conducted to identify the effectiveness of models Partial Least Square- Discriminant Analysis-(PLS-DA); Artificial Neural Network (ANN); and 1D-Convolutional neural network (CNN) for predicting fungal contamination of maize kernels in relation to orientation of germ with respect to the lens of HSI (398–1003 nm) camera. Pixel wise dataset was collected for two groups of maize kernels such as sterile (G1) and Aspergillus niger contaminated (G2) and each having 100 maize kernels imaged in germ-up (GU), germ-down (GD) and germ-randomly (GR) positioned; all the three models were trained with these datasets. It was observed that in terms of “error-rate” the prediction capability was best for GU (1.31, 1D-CNN) followed by GR (1.65, ANN) and GD (1.95, ANN). In case of a mismatch of trained and testing dataset for the various models, it was observed that GR exhibited the lowest average error rates of 5.71, 4.94 and 3.15 for PLS-DA, ANN and 1D-CNN, respectively. From the results obtained in the present study confirmed that HSI along with suitable classification technique with proper germ orientation can be used to separate the infected grains. | en_US |
dc.description.sponsorship | Not Available | en_US |
dc.language.iso | English | en_US |
dc.publisher | Elsevier | en_US |
dc.relation.ispartofseries | Not Available; | - |
dc.subject | Maize germ | en_US |
dc.subject | Fungal contamination | en_US |
dc.subject | Hyperspectral imaging | en_US |
dc.subject | PLS-DA | en_US |
dc.subject | ANN | en_US |
dc.subject | 1D-CNN | en_US |
dc.title | Effect of germ orientation during Vis-NIR hyperspectral imaging for the detection of fungal contamination in maize kernel using PLS-DA, ANN and 1D-CNN modelling | en_US |
dc.title.alternative | Not Available | en_US |
dc.type | Research Paper | en_US |
dc.publication.projectcode | Not Available | en_US |
dc.publication.journalname | Food Control | en_US |
dc.publication.volumeno | 139 | en_US |
dc.publication.pagenumber | 109077 | en_US |
dc.publication.divisionUnit | Division of Agricultural Engineering and Renewable Energy | en_US |
dc.publication.sourceUrl | https://doi.org/10.1016/j.foodcont.2022.109077 | en_US |
dc.publication.sourceUrl | https://www.sciencedirect.com/science/article/pii/S0956713522002705#section-cited-by | en_US |
dc.publication.authorAffiliation | ICAR::Central Arid Zone Research Institute | en_US |
dc.publication.authorAffiliation | ICAR::Central Institute of Agricultural Engineering | en_US |
dc.publication.authorAffiliation | Dr. Y.S.R Horticultural University, Venkataramannagudem | en_US |
dc.publication.authorAffiliation | ICAR::Central Plantation Crops Research Institute | en_US |
dc.ICARdataUseLicence | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf | en_US |
dc.publication.journaltype | Included NAAS journal list | en_US |
dc.publication.naasrating | 11.55 | en_US |
dc.publication.impactfactor | 6.652 | en_US |
Appears in Collections: | NRM-CAZRI-Publication |
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