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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
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 119
Article Submitted : 23479
Article Published : 9055
Total Authors : 24029
Total Reviewer : 831
Total Countries : 162
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| Paper Title: | CottonCare AI: Discriminative Deep Learning for Cotton Leaf Disease Detection and Remedy Recommendation. |
| Authors Name: | Pratham Suresh Sharma , Amitesh Achchhelal Yadav , Rashmi Pathak |
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IJRTI_210530
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| Published Paper Id: | IJRTI2603071 |
| Published In: | Volume 11 Issue 3, March-2026 |
| DOI: | |
| Abstract: | Cotton is one of the most important commercial crops in India and many other countries, contributing significantly to the textile industry and agricultural economy. However, cotton production is severely affected by leaf diseases and pest infestations, leading to reduced yield and financial losses for farmers. Traditional disease detection methods rely heavily on expert knowledge and manual inspection, which are time-consuming, subjective, and not scalable. Existing deep learning approaches largely depend on supervised learning techniques that require extensive labeled datasets, which are expensive and difficult to obtain in real agricultural environments. This paper presents a self-supervised learning (SSL)–based framework for cotton plant disease and pest detection using unlabeled leaf images. The proposed approach learns robust visual representations from raw cotton leaf images without manual annotation and later fine-tunes the model using a small labeled dataset. Feature embeddings extracted from the SSL model are analyzed using clustering techniques to understand disease separability. Experimental results demonstrate improved generalization, reduced annotation dependency, and robustness to real-field conditions compared to conventional supervised models. The proposed system is suitable for scalable agricultural deployment and precision farming applications. |
| Keywords: | Self-supervised learning, cotton leaf disease, agricultural image classification, plant disease detection.. |
| Cite Article: | "CottonCare AI: Discriminative Deep Learning for Cotton Leaf Disease Detection and Remedy Recommendation.", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 3, page no.a549-a555, March-2026, Available :http://www.ijrti.org/papers/IJRTI2603071.pdf |
| Downloads: | 00098 |
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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: IJRTI2603071
Registration ID:210530
Published In: Volume 11 Issue 3, March-2026
DOI (Digital Object Identifier):
Page No: a549-a555 Country: Thane, Maharashtra, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2603071 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2603071 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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