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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM64337

Grape Disease Detection Using Image Processing

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM64337

Volume:

10 |

5 |

May 2026

Pages:

1-6

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM64337

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

MM

Mahadev Mastud

Computer Engineering (Data Science)

Zeal College of Engineering and Research Narhe,Pune

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Mentors

PAk

Prof. Arunadevi kaple

Computer Engineering (Data Science)

Zeal College of Engineering and Research Narhe,Pune

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Abstract

In India, grape cultivation is both social and economic. Maharashtra is the leading grape producer in India. Grape quality has decreased in recent years due to a variety of factors. Grape infections are one of the major causes. Farmers spray massive amounts of pesticides to prevent illnesses, which raises production costs. Farmers are also unable to manually diagnose illnesses. The illnesses are only discovered after they have become infected, which takes a long time and has negative consequences for the vineyard. The goal of the proposed research is to create a monitoring system that would detect grape illnesses in their early stages using a CNN algorithm and provide notifications to the farmer and expert. The goal of this project is to provide early detection of Grape Leaf Disease. Keywords : grape leaf, Diseases Predication, CNN, Neural Network

KEYWORDS

Grape Disease Detection

Cite This Article

Mahadev Mastud, Prof. Arunadevi kaple, "Grape Disease Detection Using Image Processing," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-6, May 2026. https://doi.org/10.55041/IJSREM64337

Full Article/Conclusion

This system is on illness prevention. Pesticides are sprayed in large quantities by farmers, raising production costs. Farmers are also unable to manually diagnose illnesses. The goal of the planned research is to create a monitoring system that can detect grape disease early on. By using CNN method we can implement our grape leaf detection system. This system gives better accuracy as soon as it execute fast and gives the results fast.

References

 [1]. Anwar, SaB. Liu, C. Tan, S. Li, J. He and H. Wang, "A Data Augmentation Method Based on Generative Adversarial Networks for Grape Leaf Disease Identification," in IEEE Access, vol. 8, pp. 102188-102198, 2020, doi: 10.1109/ACCESS.2020.2998839. [2]. R. Dwivedi, S. Dey, C. Chakraborty and S. Tiwari, "Grape Disease Detection Network Based on Multi-Task Learning and Attention Features," in IEEE Sensors Journal, vol. 21, no. 16, pp. 17573-17580, 15 Aug.15, 2021, doi: 10.1109/JSEN.2021.3064060. [3].  S. Lauguico, R. Concepcion, R. Ruzcko Tobias, A. Bandala, R. Rhay Vicerra and E. Dadios, "Grape Leaf Multi-disease Detection with Confidence Value Using Transfer Learning Integrated to Regions with Convolutional Neural Networks," 2020 IEEE REGION 10 CONFERENCE (TENCON), 2020, pp. 767-772, doi: 10.1109/TENCON50793.2020.9293866. [4]. S. M. Jaisakthi, P. Mirunalini, D. Thenmozhi and Vatsala, "Grape Leaf Disease Identification using Machine Learning Techniques," 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), 2019, pp. 1-6, doi: 10.1109/ICCIDS.2019.8862084. [5]. Sanath Rao, U., Swathi, R., Sanjana, V., Arpitha, L., Chandrasekhar, K., Chinmayi, & Naik, P. K. (2021). International Conference on Computing System and its Applications (ICCSA - 2021). Global                             Transitions  Proceedings. doi:10.1016/j.gltp.2021.08.002 [6]. S. Barburiceanu, R. Terebes and S. Meza, "Grape Leaf Disease Classification using LBP-derived Texture Operators and Colour," 2020 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR), 2020, pp.     1-6,        doi: 10.1109/AQTR49680.2020.9130019. [7]. A. A. Bharate and M. S. Shirdhonkar, "Classification of Grape Leaves using KNN and SVM Classifiers," 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), 2020, pp. 745-749, doi:   10.1109/ICCMC48092.2020.ICCMC- 000139. [8]. Liu, Bin; Ding, Zefeng; Tian, Liangliang; He, Dongjian; Li, Shuqin; Wang, Hongyan (2020). Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks. Frontiers in Plant Science, 11(), 1082–. doi:10.3389/fpls.2020.01082 [9]. K. Z. Thet, K. K. Htwe and M. M. Thein, "Grape Leaf Diseases Classification using Convolutional Neural Network," 2020 International Conference on Advanced Information Technologies (ICAIT), 2020, pp. 147-152, doi: 10.1109/ICAIT51105.2020.9261801. [10]. Y. Nagaraju, Venkatesh, S. Swetha and S. Stalin, "Apple and Grape Leaf Diseases Classification using Transfer Learning via Fine-tuned Classifier," 2020 IEEE International Conference on Machine Learning and Applied Network Technologies (ICMLANT), 2020, pp. 1-6, doi: 10.1109/ICMLANT50963.2020.9355991.

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Publication Date

5/30/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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