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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Volume 11 | Issue 3
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
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| Paper Title: | Automated Weed Identification in Vegetable Farming using Image Processing and Deep Learning |
| Authors Name: | Ch.Lavanya Susanna , N. Sujitha , E. Sri Harshitha , N. Lakshmi Prasanna , G. Samuel |
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IJRTI_189325
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| Published Paper Id: | IJRTI2403042 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | |
| Abstract: | Vegetable farming pose a bigger problem for weed detection than crop plantations due to their unpredictable plant spacing. Up until now, not much has been studied about weed identification in vegetable plantations. Because weed species vary widely, direct weed identification was the main method employed in previous crop weed identification techniques. On the other hand, this paper presents a revolutionary approach that combines image processing with deep learning technology. The ability to quickly locate, recognize, and identify objects in images or videos has been made feasible by the rapid development of Deep Learning (DL) algorithms. Currently, DL techniques are used for a wide range of farming and agricultural jobs, providing the possibility of higher yields by effectively managing weeds with the use of automatic detection and classification systems. The problem of weed detection in crops stems from the fact that weeds and crops have similar colors, or "green-on-green," and that their growth phases also share a remarkable amount of shape and texture. Furthermore, a plant that is seen as a crop in one setting may be viewed as a weed in another. We evaluate deep learning systems using crop and weed datasets as part of our research. The technology uses CNN-style deep learning algorithms to forecast crops or weeds. Delineating border boxes for expected weeds and crops is accomplished by using YOLO. To sum up, the experimental findings highlight several critical performance metrics, such as accuracy and mistake rate. |
| Keywords: | Weed detection, Smart farming, deep learning, image processing, CNN, YOLO. |
| Cite Article: | "Automated Weed Identification in Vegetable Farming using Image Processing and Deep Learning ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.9, Issue 3, page no.287 - 292, March-2024, Available :http://www.ijrti.org/papers/IJRTI2403042.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2403042
Registration ID:189325
Published In: Volume 9 Issue 3, March-2024
DOI (Digital Object Identifier):
Page No: 287 - 292 Country: vijayawada-8,krishna dist, Andhra Pradesh, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2403042 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2403042 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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