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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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Issue: June 2026
Volume 11 | Issue 6
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 121
Article Submitted : 24737
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Total Authors : 24875
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| Paper Title: | Weed Detection Using Convolutional Neural Networks (CNNs) |
| Authors Name: | Ansh Yadav , Anurag Panchal , Anurag Solanki , Anushka , Ashish Shukla |
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IJRTI_212292
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| Published Paper Id: | IJRTI2605039 |
| Published In: | Volume 11 Issue 5, May-2026 |
| DOI: | |
| Abstract: | In Agriculture, farmers plant different kinds of crops and grow them to sell or use, but apart from the intended crops there are also harmful weeds that grow naturally and compete with the crops for essential resources like nutrients, sunshine, water and space thus, resulting in poor quality and less yield of the crops. To Solve this recurring problem accurate and efficient weed detection is crucial as it helps in precision farming, however conventional detection and removal techniques are labour-intensive, time-consuming, and frequently ineffective. Following the recent advancements in deep learning and artificial intelligence many automated methods to tackle this problem have been developed. In this study, we propose a Convolutional Neural Network (CNNs) based deep learning model which uses TensorFlow framework and MobileNetV2 architecture to extract key features from captured plant images and automatically and accurately detecting and classifying the weeds and their species from the actual crop plants. The Deep Weeds dataset upon which the model is trained and evaluated, was acquired from GitHub. The Dataset is a multiclass weed species image dataset containing 17,509 labelled images across 9 distinct species. This study aims to advance intelligent and scalable automated precision agriculture techniques using a lightweight, efficient and easy to implement machine learning model that can be used by everyone in the agriculture industry. |
| Keywords: | Weed Detection, Convolutional Neural Networks, MobileNetV2, Deep Learning, Precision Agriculture, Image Classification, TensorFlow |
| Cite Article: | " Weed Detection Using Convolutional Neural Networks (CNNs)", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.a342-a345, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605039.pdf |
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| ISSN: |
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: IJRTI2605039
Registration ID:212292
Published In: Volume 11 Issue 5, May-2026
DOI (Digital Object Identifier):
Page No: a342-a345 Country: Greater Noida, Uttar Pradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2605039 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2605039 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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