KRISHI
ICAR RESEARCH DATA REPOSITORY FOR KNOWLEDGE MANAGEMENT
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Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/71626
Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Md. Ashraful Haque | en_US |
dc.contributor.author | Sudeep Marwaha | en_US |
dc.contributor.author | Chandan Kumar Deb | en_US |
dc.contributor.author | Sapna Nigam | en_US |
dc.contributor.author | Alka Arora | en_US |
dc.contributor.author | Karambir Singh Hooda | en_US |
dc.contributor.author | P. Lakshmi Soujanya | en_US |
dc.contributor.author | Sumit Kumar Aggarwal | en_US |
dc.contributor.author | Brejesh Lall | en_US |
dc.contributor.author | Mukesh Kumar | en_US |
dc.contributor.author | Shahnawazul Islam | en_US |
dc.contributor.author | Mohit Panwar | en_US |
dc.contributor.author | Prabhat Kumar | en_US |
dc.contributor.author | R. C. Agrawal | en_US |
dc.date.accessioned | 2022-04-19T07:38:39Z | - |
dc.date.available | 2022-04-19T07:38:39Z | - |
dc.date.issued | 2022-04-15 | - |
dc.identifier.citation | Haque, M.A., Marwaha, S., Deb, C.K. et al. Deep learning-based approach for identification of diseases of maize crop. Sci Rep 12, 6334 (2022). https://doi.org/10.1038/s41598-022-10140-z | en_US |
dc.identifier.issn | Not Available | - |
dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/71626 | - |
dc.description | Not Available | en_US |
dc.description.abstract | In recent years, deep learning techniques have shown impressive performance in the field of identification of diseases of crops using digital images. In this work, a deep learning approach for identification of in‑field diseased images of maize crop has been proposed. The images were captured from experimental fields of ICAR‑IIMR, Ludhiana, India, targeted to three important diseases viz. Maydis Leaf Blight, Turcicum Leaf Blight and Banded Leaf and Sheath Blight in a non‑destructive manner with varied backgrounds using digital cameras and smartphones. In order to solve the problem of class imbalance, artificial images were generated by rotation enhancement and brightness enhancement methods. In this study, three different architectures based on the framework of ‘Inception‑v3’ network were trained with the collected diseased images of maize using baseline training approach. The best‑performed model achieved an overall classification accuracy of 95.99% with average recall of 95.96% on the separate test dataset. Furthermore, we compared the performance of the best‑performing model with some pre‑trained state‑of‑the‑art models and presented the comparative results in this manuscript. The results reported that best‑performing model performed quite better than the pre‑trained models. This demonstrates the applicability of baseline training approach of the proposed model for better feature extraction and learning. Overall performance analysis suggested that the best‑performed model is efficient in recognizing diseases of maize from in‑field images even with varied backgrounds. | en_US |
dc.description.sponsorship | Not Available | en_US |
dc.language.iso | English | en_US |
dc.publisher | Not Available | en_US |
dc.relation.ispartofseries | Not Available; | - |
dc.subject | Deep Learning | en_US |
dc.subject | Convolutional Neural Network | en_US |
dc.subject | Disease Diagnosis | en_US |
dc.subject | Maize crop | en_US |
dc.subject | Image Recognition | en_US |
dc.title | Deep learning-based approach for identification of diseases of maize crop | 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 | Scientific Reports | en_US |
dc.publication.volumeno | 12 | en_US |
dc.publication.pagenumber | Not Available | en_US |
dc.publication.divisionUnit | Not Available | en_US |
dc.publication.sourceUrl | https://doi.org/10.1038/s41598-022-10140-z | en_US |
dc.publication.authorAffiliation | ICAR-Indian Agricultural Statistics Research Institute | en_US |
dc.publication.authorAffiliation | ICAR-National Bureau of Plant Genetic Resources | en_US |
dc.publication.authorAffiliation | ICAR-Indian Institute of Maize Research | en_US |
dc.publication.authorAffiliation | Indian Institute of Technology Delhi | en_US |
dc.publication.authorAffiliation | National Agricultural Higher Education Project | en_US |
dc.ICARdataUseLicence | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf | en_US |
dc.publication.naasrating | 10.38 | en_US |
dc.publication.impactfactor | 4.38 | en_US |
Appears in Collections: | AEdu-IASRI-Publication |
Files in This Item:
File | Description | Size | Format | |
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Deep learning-based approach for identification of diseases of maize crop _ Enhanced Reader.pdf | 23.76 MB | Adobe PDF | View/Open |
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