ISSN2320-2882
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ESTD2013
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Paper Title

A Real Time Monitoring System for Accurate Plant Leaves Disease Detection using Deep Learning

Authors

Kapase Pratiksha Dnyaneshwar , Vaishali Nana Padmor , Devan Ramdas Fulsundar , Pranali Suresh Gunjal , Sonali T. Benke

Keywords

Plant leaf disease detection, real-time monitoring, deep learning, convolutional neural network (CNN), IoT, image processing, smart agriculture, edge computing

Abstract

Accurate and early identification of plant leaf diseases plays a crucial role in improving crop yield, ensuring sustainable farming, and reducing economic losses. This paper presents a real-time monitoring system for precise plant leaf disease detection using deep learning techniques. The proposed system integrates IoT-enabled cameras for continuous image acquisition, an efficient pre-processing pipeline for noise removal and segmentation, and a Convolutional Neural Network (CNN) optimized for deployment on edge computing devices. The system performs rapid and accurate classification of diseased and healthy leaves, providing farmers with instant alerts and visual insights through a user-friendly dashboard. Experimental validation confirms the robustness of the proposed model across multiple crop species and environmental conditions. This study contributes to the advancement of smart agriculture by providing an intelligent, automated, and cost-effective solution for real-time disease detection and management.

How To Cite

Choose the style your journal or department asks for, then copy it. Every version below is generated from this paper's own record.

IJCRT — journal style
"A Real Time Monitoring System for Accurate Plant Leaves Disease Detection using Deep Learning", IJCRT - International Journal of Creative Research Thoughts (www.ijcrt.org), ISSN:2320-2882, Vol.13, Issue 11, page no.b874-b884, November 2025, Available :https://ijcrt.org/papers/IJCRT2511239.pdf
APA — 7th edition
Dnyaneshwar, K. P., Padmor, V. N., Fulsundar, D. R., Gunjal, P. S., & Benke, S. T. (2025). A Real Time Monitoring System for Accurate Plant Leaves Disease Detection using Deep Learning. International Journal of Creative Research Thoughts, 13(11), b874-b884. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2511239
MLA — 9th edition
Dnyaneshwar, Kapase Pratiksha, et al. "A Real Time Monitoring System for Accurate Plant Leaves Disease Detection using Deep Learning." International Journal of Creative Research Thoughts, vol. 13, no. 11, 2025, pp. b874-b884, https://ijcrt.org/viewpaperforall.php?paper=IJCRT2511239.
Chicago — 17th, bibliography
Dnyaneshwar, Kapase Pratiksha, et al. "A Real Time Monitoring System for Accurate Plant Leaves Disease Detection using Deep Learning." International Journal of Creative Research Thoughts 13, no. 11 (2025): b874-b884. https://ijcrt.org/viewpaperforall.php?paper=IJCRT2511239.
Harvard — author–date
Dnyaneshwar, K.P. et al. (2025) 'A Real Time Monitoring System for Accurate Plant Leaves Disease Detection using Deep Learning', International Journal of Creative Research Thoughts, 13(11), pp. b874-b884. Available at: https://ijcrt.org/viewpaperforall.php?paper=IJCRT2511239
IEEE — numbered reference
K. P. Dnyaneshwar, V. N. Padmor, D. R. Fulsundar, P. S. Gunjal and S. T. Benke, "A Real Time Monitoring System for Accurate Plant Leaves Disease Detection using Deep Learning," IJCRT, vol. 13, no. 11, pp. b874-b884, Nov. 2025.
Vancouver — biomedical
Dnyaneshwar KP, Padmor VN, Fulsundar DR, Gunjal PS, Benke ST. A Real Time Monitoring System for Accurate Plant Leaves Disease Detection using Deep Learning. IJCRT. 2025 Nov;13(11):b874-b884.
AMA — 11th edition
Dnyaneshwar KP, Padmor VN, Fulsundar DR, Gunjal PS, Benke ST. A Real Time Monitoring System for Accurate Plant Leaves Disease Detection using Deep Learning. IJCRT. 2025;13(11):b874-b884. Accessed at https://ijcrt.org/viewpaperforall.php?paper=IJCRT2511239
BibTeX — Zotero, Mendeley, LaTeX
@article{IJCRT2511239, author = {Kapase Pratiksha Dnyaneshwar and Vaishali Nana Padmor and Devan Ramdas Fulsundar and Pranali Suresh Gunjal and Sonali T. Benke}, title = {A Real Time Monitoring System for Accurate Plant Leaves Disease Detection using Deep Learning}, journal = {International Journal of Creative Research Thoughts}, volume = {13}, number = {11}, pages = {b874-b884}, year = {2025}, month = {November}, issn = {2320-2882}, url = {https://ijcrt.org/viewpaperforall.php?paper=IJCRT2511239} }
RIS — EndNote, RefWorks
TY - JOUR AU - Dnyaneshwar, Kapase Pratiksha AU - Padmor, Vaishali Nana AU - Fulsundar, Devan Ramdas AU - Gunjal, Pranali Suresh AU - Benke, Sonali T. TI - A Real Time Monitoring System for Accurate Plant Leaves Disease Detection using Deep Learning T2 - International Journal of Creative Research Thoughts JA - IJCRT VL - 13 IS - 11 PY - 2025 SN - 2320-2882 UR - https://ijcrt.org/viewpaperforall.php?paper=IJCRT2511239 SP - b874-b884 ER -

Issue

Volume 13 Issue 11, November-2025
Pages : b874-b884

Article Information

Pages
b874-b884
Published
November 2025
Registration ID
IJCRT_296327
Published Paper ID
IJCRT2511239
Research Area
Science and Technology
Author Type
Indian Author
Location
Nashik, Maharashtra, India
Licence
CC BY 4.0 · Open Access
ISSN & Impact Factor
2320-2882 · IF 9.17
Downloads
193

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International Journal of Creative Research Thoughts Published by IJCRT Publication
2320-2882 ISSN
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Licence

© 2025 — Authors hold the copyright of this article. This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
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Declarations

Funding

No external funding was received for this study.

Conflict of Interest

The authors declare that they have no conflict of interest.

Acknowledgements

The authors would like to thank the reviewers and the editorial board of International Journal of Creative Research Thoughts for their careful reading and constructive comments, and all colleagues who supported the preparation of this manuscript.

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