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Multi-Class Plant Leaf Disease Detection: A CNN-based Approach with Mobile App Integration

by Md Aziz Hosen Foysal, Foyez Ahmed, Md Zahurul Haque
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 186 - Number 41
Year of Publication: 2024
Authors: Md Aziz Hosen Foysal, Foyez Ahmed, Md Zahurul Haque
10.5120/ijca2024924026

Md Aziz Hosen Foysal, Foyez Ahmed, Md Zahurul Haque . Multi-Class Plant Leaf Disease Detection: A CNN-based Approach with Mobile App Integration. International Journal of Computer Applications. 186, 41 ( Sep 2024), 62-68. DOI=10.5120/ijca2024924026

@article{ 10.5120/ijca2024924026,
author = { Md Aziz Hosen Foysal, Foyez Ahmed, Md Zahurul Haque },
title = { Multi-Class Plant Leaf Disease Detection: A CNN-based Approach with Mobile App Integration },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2024 },
volume = { 186 },
number = { 41 },
month = { Sep },
year = { 2024 },
issn = { 0975-8887 },
pages = { 62-68 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume186/number41/multi-class-plant-leaf-disease-detection-a-cnnbased-approach-with-mobile-app-integration/ },
doi = { 10.5120/ijca2024924026 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-09-30T23:02:32.022563+05:30
%A Md Aziz Hosen Foysal
%A Foyez Ahmed
%A Md Zahurul Haque
%T Multi-Class Plant Leaf Disease Detection: A CNN-based Approach with Mobile App Integration
%J International Journal of Computer Applications
%@ 0975-8887
%V 186
%N 41
%P 62-68
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Plant diseases significantly impact agricultural productivity, resulting in economic losses and food insecurity. Prompt and accurate detection is crucial for the efficient management and mitigation of plant diseases. This study investigates advanced techniques in plant disease detection, emphasizing the integration of image processing, machine learning, deep learning methods, and mobile technologies. High-resolution images of plant leaves were captured and analyzed using convolutional neural networks (CNNs) to detect symptoms of various diseases, such as blight, mildew, and rust. This study explores 14 classes of plants and diagnoses 26 unique plant diseases. This study focuses on common diseases affecting various crops. The model was trained on a diverse dataset encompassing multiple crops and disease types, achieving 98.14% accuracy in disease diagnosis. Finally integrated this model into mobile apps for real-time disease diagnosis.

References
  1. Bhattarai, S. (2018). New Plant Diseases Dataset. Kaggle. [Online]. Available: https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset.
  2. Shrestha, G., Deepsikha, Das, M., & Dey, N. (2020). Plant Disease Detection Using CNN. 2020 IEEE Applied Signal Processing Conference (ASPCON), Kolkata, India, 109-113. https://doi.org/10.1109/ASPCON49795.2020.9276722.
  3. Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318. https://doi.org/10.1016/j.compag.2018.01.009.
  4. Deepalakshmi, P., Krishna, P. T., Chandana, S. S., & Srinivasu, P. N. (2021). Plant leaf disease detection using CNN algorithm. International Journal of Information System Modeling and Design, 12(1), 1–21. https://doi.org/10.4018/ijismd.2021010101.
  5. Sardogan, M., Tuncer, A., & Ozen, Y. (2018). Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm. 2018 3rd International Conference on Computer Science and Engineering (UBMK), Sarajevo, Bosnia and Herzegovina, 382-385. https://doi.org/10.1109/UBMK.2018.8566635.
  6. Wallelign, S., Polceanu, M., & Buche, C. (2018, May). Soybean plant disease identification using convolutional neural network. The Thirty-first International FLAIRS Conference.
  7. Agarwal, M., Gupta, S. K., & Biswas, K. K. (2020). Development of Efficient CNN model for Tomato crop disease identification. Sustainable Computing: Informatics and Systems, 28, 100407. https://doi.org/10.1016/j.suscom.2020.100407.
  8. Wang, D., Wang, J., Li, W., & Guan, P. (2021). T-CNN: Trilinear convolutional neural networks model for visual detection of plant diseases. Computers and Electronics in Agriculture, 190, 106468. https://doi.org/10.1016/j.compag.2021.106468.
  9. Joshi, M., & Bhavsar, H. (2023). Deep Learning Technology based Night-CNN for Nightshade Crop Leaf Disease Detection. International Journal of Intelligent Systems and Applications in Engineering, 11(1), 215–227. https://www.ijisae.org/index.php/IJISAE/article/view/2461.
  10. Thakur, P. S., Sheorey, T., & Ojha, A. (2023). VGG-ICNN: A Lightweight CNN model for crop disease identification. Multimed Tools Appl, 82, 497–520. https://doi.org/10.1007/s11042-022-13144-z.
  11. Lu, J., Tan, L., & Jiang, H. (2021). Review on Convolutional Neural Network (CNN) Applied to Plant Leaf Disease Classification. Agriculture, 11(8), 707. https://doi.org/10.3390/agriculture11080707.
  12. Rao, D. S., Ch, R. B., Kiran, V. S., Rajasekhar, N., Srinivas, K., et al. (2022). Plant disease classification using deep bilinear CNN. Intelligent Automation & Soft Computing, 31(1), 161-176. https://doi.org/10.32604/iasc.2022.017706.
  13. Suresh, G., Gnanaprakash, V., & Santhiya, R. (2019). Performance Analysis of Different CNN Architecture with Different Optimisers for Plant Disease Classification. 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), Coimbatore, India, 916-921. https://doi.org/10.1109/ICACCS.2019.8728282.
  14. Gokulnath, B. V., & Usha Devi, G. (2021). Identifying and classifying plant disease using resilient LF-CNN. Ecological Informatics, 63, 101283. https://doi.org/10.1016/j.ecoinf.2021.101283.
  15. Sun, X., Li, G., Qu, P., Xie, X., Pan, X., & Zhang, W. (2022). Research on plant disease identification based on CNN. Cognitive Robotics, 2, 155-163. https://doi.org/10.1016/j.cogr.2022.07.001.
  16. Singh, A. K., Khan, R., et al. (2022). Hybrid Feature-Based Disease Detection in Plant Leaf Using Convolutional Neural Network, Bayesian Optimized SVM, and Random Forest Classifier. Journal of Food Quality, 2022, 1–16. https://doi.org/10.1155/2022/2845320.
  17. Ma, L., et al. (2023). Maize Leaf Disease Identification Based on YOLOv5n Algorithm Incorporating Attention Mechanism. Agronomy, 13, 521. https://doi.org/10.3390/agronomy13020521.
  18. Al-bayati, J. S. H., & Ustündağ, B. B. (2020). Evolutionary feature optimization for plant leaf disease detection by deep neural networks. International Journal of Computational Intelligence Systems, 13(1), 12. [Online]. Available: https://doi.org/10.32604/iasc.2022.017706
  19. Geetharamani, G., & Arun Pandian, J. (2019). Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Computers and Electrical Engineering, 76, 323–338. https://doi.org/10.1016/j.compeleceng.2019.04.011.
  20. Kawatra, M., Agarwal, S., & Kapur, R. (2020). Leaf Disease Detection using Neural Network Hybrid Models. 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), 225–230. https://doi.org/10.1109/ICCCA49541.2020.9250885.
Index Terms

Computer Science
Information Sciences

Keywords

Convolutional Neural Network Leaf Classification Disease Diagnosis Mobile Technologies

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