CLASSIFICATION OF PLANT LEAF DISEASES USING DEEP LEARNING CONCEPTS

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 12 | 02 | Page :
    By

    Bushra Sehar,

  • Bushra Sehar,

  1. , Muffakham Jah College of Engineering and Technology, Hyderabad, Telangana, India
  2. , Muffakham Jah College of Engineering and Technology, Hyderabad, Telangana, India

Abstract

document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_abs_202031’);});Edit Abstract & Keyword

Agriculture is vital to the economy of a country like India, where 70% of the workforce is employed in this sector. Plants suffering from illnesses experience a significant reduction in output. Delays in the identification of plant diseases lead to decreased yield and plant mortality. The cost of manufacturing is increased since it takes a big number of experts to manually detect plant diseases over several acres of land. The purpose is to summarize a critical challenge in agriculture by Automating the diagnosis of plant leaf diseases with the potential to revolutionize crop management Traditional methods are often time- consuming and complex. leading to healthier crops and a more sustainable future. The proposed work implements a convolutional neural network model, VGG-16 for training the system. The data is pre- processed to meet the requirements of the input. The VGG-16 is compared to other models such as SVM, KNN, and traditional CNN & FCNN.

Keywords: Plant diseases, Leaf Images, VGG16, Pre- trained model, Transfer Learning, FCNN.

How to cite this article:
Bushra Sehar, Bushra Sehar. CLASSIFICATION OF PLANT LEAF DISEASES USING DEEP LEARNING CONCEPTS. Journal of Image Processing & Pattern Recognition Progress. 2025; 12(02):-.
How to cite this URL:
Bushra Sehar, Bushra Sehar. CLASSIFICATION OF PLANT LEAF DISEASES USING DEEP LEARNING CONCEPTS. Journal of Image Processing & Pattern Recognition Progress. 2025; 12(02):-. Available from: https://journals.stmjournals.com/joipprp/article=2025/view=0


document.addEventListener(‘DOMContentLoaded’,function(){frmFrontForm.scrollToID(‘frm_container_ref_202031’);});Edit

References

  1. Harshavardhan K, Abhinav KrishnaP V J and Angelina Geetha 2023 Detection of Various Plant Leaf Diseases Using Deep Learning Techniques.  In: Proceedings of International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), https://doi.org/10.1109/ACCAI58221.2023.102000 31
  2. Kelothu Shivaprasad, and Ankita Wadhawan 2023 Deep Learning-based Plant Leaf Disease Detection. In: Proceedings of 7th International Conference on Intelligent Computing and Control Systems (ICICCS). https://doi.org/10.1109/ICICCS56967.2023.101428 57
  3. Amine Mezenner, Hassiba Nemmour, Youcef Chibani and Adel Hafiane 2022 Tomato Plant Leaf Disease Classification based on CNN features and Support Vector Machines. In: Proceedings of 2nd International Conference on Advanced Electrical Engineering (ICAEE), https://doi.org/10.1109/ICAEE53772.2022.996207 0
  4. Almira Suljovic Stevan Cakic, and Tomo Popovic 2022 Detection of Plant Diseases Using Leaf Images and Machine Learning.   In: Proceedings of 21st International Symposium INFOTEH- JAHORINA (INFOTEH)https://doi.org/10.1109/INFOTEH5373 7.2022.9751245
  5. Rajesh T R, Gowri V., and Byusing 2021 Enhanced Approach for Disease Prediction in Sugarcane Crop with the support of advanced machine learning strategies. In: Proceedings of (2021), vol. 25, Issue 4, 2021, Pages. 16805-16814, Annals of R.S.C.B.
  6. Lakshman Rao A, Raja Babu M, and T Ravi Kiran 2024 Plant disease prediction and classification using deep learning Convnets. In: Proceedings of International Journal of Research Publication and Reviews, vol. 5, Issue 5, pp. 10877-10885
  7. Draško Radovanovic, and Slobodan Đukanovic 2020 Image-Based Plant Disease Detection: A Comparison of Deep Learning and Classical Machine Learning Algorithms. In: Proceedings of International Scientific-Professional Conference on Information Technology (IT) , https://doi.org/10.1109/IT48810.2020.9070664
  8. S. Santhana Hari, Sivakumar M, Renuga P, Karthikeyan S., and Suriya S 2020 Detection of Plant Disease by Leaf Image Using Convolutional Neural Network. In: Proceedings of International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), https://doi.org/10.1109/IT48810.2020.9070664
  9. Yashwant Kurmi, Suchi Gangwar, Suchi Gangwar, Dheeraj Agrawal, and Satrughan Kumar 2020, Leaf image analysis based on crop disease classification , Signal Image and Video Processing Journal http://dx.doi.org/10.1007/s11760-020-01780-7
  10. Yaser N. , Zhang, Defu, Chen, Junde, and Tian, Yuan 2020 Recognition of plant leaf diseases based on computer vision, Journal of Ambient Intelligence and Humanized Computing
  11. Nanebkara Sue Han Lee, Hervé Goeau, Pierre Bonnet and Alexis Joly 2020 New perspective on plant disease characterization based on deep learning, ACM digital library, vol. 170, Issue C, https://doi.org/10.1016/j.compag.2020.105220 2020) DO – 10.1007/s12652-020-02505-x
  12. Mehmet Metin Ozguven, and Kemal Adem 2019 Automatic detection & classification of leaf spot disease in sugar beet using deep learning,, In: Proceedings of Physical A: Statistical Mechanics and its Applications, vol. 535, https://doi.org/10.1016/j.physa.2019.122537
  13. Francis Jobin, Dhas Anto;Kadan, and Anoop 2017 Identification of leaf diseases in pepper plants using soft computing techniques, In: Proceedings of Conference on Emerging Devices and Smart Systems (ICEDSS). DO – 10.1109/ICEDSS.2016.7587787 , pp.168 – 173
  14. Singh Vijaj, Singh Varsha, and Misra A 2015 Detection of unhealthy region of plant leaves using image pre_processing & genetic algorithm. In: Proceedings of International Conference on Advances in Computer Engineering and Applications (ICACEA) DOI- 10.1109/ICACEA.2015.716485
  15. Azfar Saeed, Nadeem Al Hassan, Adnan, and Shaikh Abdul Basit 2015 Pest monitoring & control system using Wireless Sensor Network. Journal of Entomology and Zoology Studies, vol. 3, issue 2, pp. 92-99.
  16. SH Mahin, F Taranum, LN Fatima, KU Khan, Detection and interception of black hole attack with justification using anomaly based intrusion detection system in MANETs,  International Journal of Recent Technology and Engineering, vol. 8, issue 11, pp. 2392-2398,2019
Ahead of Print Subscription Review Article
Volume 12
02
Received 28/07/2025
Accepted 06/08/2025
Published 12/08/2025
Publication Time 15 Days
143

[first_name] [last_name]

My IP

PlumX Metrics