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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: February 2026
Volume 11 | Issue 2
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 117
Article Submitted : 21318
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Total Authors : 22301
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| Paper Title: | Vision Mamba s-power System for Accurate Plant Leaf Disease Detection |
| Authors Name: | Rutuja Raju Yete , Vansh Zalpuri , Bhavesh Sopan Uchade , Sahil Dnyaneshwar Unhale , Dattatray G Takale |
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IJRTI_208045
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| Published Paper Id: | IJRTI2601039 |
| Published In: | Volume 11 Issue 1, January-2026 |
| DOI: | |
| Abstract: | Plant diseases account for nearly a third of the annual crop yield in India. This leads to several economic setbacks for farmers and the national food security status of the country. Early and accurate disease identification is the only way to prevent such losses, but laboratory facilities, expert guidance, and timely diagnosis are inaccessible to most farmers. The situation cries for affordable, scalable, and reliable technological solutions that do not need a lab environment but can work in real farming conditions.Over the last few years, the combination of Artificial Intelligence and Computer Vision has been a promising and effective technology in automating the task of plant disease identification using images of leaves. However, as it stands, the limitation of this system is that they have very few diverse datasets collected from real-field scenarios, which has proven to be a bottleneck in the performance of these systems. Most of the datasets are collected under controlled conditions and therefore do not reflect the complexities of real farms—such as lighting, background, leaf orientation, occlusion, etc. By providing annotated images of real-world scenarios, datasets such as PlantDoc have been instrumental in improving model accuracy, with many studies reporting a gain of more than 30% when models are trained on field data.This project has developed a Vision Mamba-S Powered Plant Leaf Disease Detection System to take these breakthroughs further. Vision Mamba is an innovative architecture based on selective state-space modeling that allows the model to not only get very detailed local pattern from the leaf images but also the larger context structures in the image. The advantages of Vision Mamba over traditional CNNs and transformer-based models include computational efficiency, speed of inference, and smaller memory size, thus a very suitable candidate for deployment on mobile devices and IoT platforms.The device proposed takes the leaf pictures to be analyzed, using Vision Mamba-S, extracts discriminating features, and then through classification methods, identifies the diseases with high accuracy, even in the most challenging situations in the field. Since it is a very small application, it can work in real-time, offline, and is, therefore, a solution that farmers in faraway places can take advantage of without the need for expensive devices or constant internet access. |
| Keywords: | Plant Disease Detection, Vision Mamba-S, Computer Vision, Artificial Intelligence, Real-Field Dataset, PlantDoc, State-Space Modeling . |
| Cite Article: | "Vision Mamba s-power System for Accurate Plant Leaf Disease Detection", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.11, Issue 1, page no.a301-a308, January-2026, Available :http://www.ijrti.org/papers/IJRTI2601039.pdf |
| Downloads: | 000118 |
| 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: IJRTI2601039
Registration ID:208045
Published In: Volume 11 Issue 1, January-2026
DOI (Digital Object Identifier):
Page No: a301-a308 Country: Pune , MAHARASTRA, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2601039 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2601039 |
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ISSN: 2456-3315
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