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/123456789/82016
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Akshay Dheeraj | en_US |
dc.contributor.author | Satish Chand | en_US |
dc.date.accessioned | 2024-04-12T17:06:10Z | - |
dc.date.available | 2024-04-12T17:06:10Z | - |
dc.date.issued | 2023-01-01 | - |
dc.identifier.citation | Dheeraj, A., Chand, S. (2023). Using Deep Learning Models for Crop and Weed Classification at Early Stage. In: Shakya, S., Du, KL., Ntalianis, K. (eds) Sentiment Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1432. Springer, Singapore. https://doi.org/10.1007/978-981-19-5443-6_69 | en_US |
dc.identifier.issn | 978-981-19-5443-6 | - |
dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/82016 | - |
dc.description | Not Available | en_US |
dc.description.abstract | Agriculture is essential for human existence, and it plays an important role in the world economy. There is increasing demand for food to feed the everincreasing world population. Agriculture is affected by climate changes along with weed control. Weeds are unwanted plants that compete with plants for nutrition, and sunlight and adversely affect crop quality and production. Manual weeding is a tedious and labor-intensive task because both crop and weed look the same by visual appearance. Artificial intelligence techniques like deep learning can address this problem of crop and weed classification. In this research work, a deep learningbased classification system has been proposed to classify the weed and crop based on RGB images. We investigated two popular deep learning-based transfer learning models, namely DenseNet169 and MobileNetV2, and assessed their performances for crop and weed recognition. These models perform excellently with an accuracy of 97.14 and 94.92%, respectively. The significant accuracy results make the model an important tool for farmers to identify weeds. | en_US |
dc.description.sponsorship | Not Available | en_US |
dc.language.iso | English | en_US |
dc.publisher | Springer Singapore | en_US |
dc.relation.ispartofseries | Advances in Intelligent Systems and Computing; | - |
dc.subject | Deep Learning | en_US |
dc.subject | Weed Classification | en_US |
dc.title | Using Deep Learning Models for Crop and Weed Classification at Early Stage | en_US |
dc.title.alternative | Not Available | en_US |
dc.type | Book chapter | en_US |
dc.publication.projectcode | Not Available | en_US |
dc.publication.journalname | Not Available | en_US |
dc.publication.volumeno | Not Available | en_US |
dc.publication.pagenumber | 21-32 | en_US |
dc.publication.divisionUnit | Not Available | en_US |
dc.publication.sourceUrl | https://doi.org/10.1007/978-981-19-5443-6_69 | en_US |
dc.publication.authorAffiliation | ICAR::Indian Agricultural Statistics Research Institute | en_US |
dc.publication.authorAffiliation | JNU | en_US |
dc.ICARdataUseLicence | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf | en_US |
dc.publication.journaltype | Not Available | en_US |
dc.publication.naasrating | Not Available | en_US |
dc.publication.impactfactor | Not Available | en_US |
Appears in Collections: | AEdu-IASRI-Publication |
Files in This Item:
File | Description | Size | Format | |
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ICSADL Paper_merged.pdf | 783.41 kB | Adobe PDF | View/Open |
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