EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 54 (2023) ITM Web Conf., 54 (2023) 01014 References
Open Access
Issue
ITM Web Conf.
Volume 54, 2023
2nd International Conference on Advances in Computing, Communication and Security (I3CS-2023)
Article Number 01014
Number of page(s) 10
Section Computing
DOI https://doi.org/10.1051/itmconf/20235401014
Published online 04 July 2023
  1. Pandian, J. Arun, V. Dhilip Kumar, Oana Geman, Mihaela Hnatiuc, Muhammad Arif, and K. Kanchanadevi. “Plant disease detection using deep convolutional neural network.” Applied Sciences 12, no. 14 (2022): 6982. [CrossRef] [Google Scholar]
  2. Islam, Farabee, Md Nazmul Hoq, and Chowdhury Mofizur Rahman. “Application of transfer learning to detect potato disease from leaf image.” In 2019 IEEE International Conference on Robotics, Automation, Artificialintelligence and Internet-of-Things (RAAICON), pp. 127–130. IEEE, 2019. [Google Scholar]
  3. Kamal, K. C., Zhendong Yin, Bo Li, Bo Ma, and Mingyang Wu. “Transfer learning for fine-grained crop disease classification based on leaf images.” In 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1–5. IEEE, 2019. [Google Scholar]
  4. Ozguven, Mehmet Metin, and Kemal Adem. “Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms.” Physica A: statistical mechanics and its applications 535 (2019): 122537. [Google Scholar]
  5. Sibiya, Malusi, and Mbuyu Sumbwanyambe. “A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks.” AgriEngineering 1, no. 1 (2019): 119–131. [CrossRef] [Google Scholar]
  6. Pandian, J. Arun, V. Dhilip Kumar, Oana Geman, Mihaela Hnatiuc, Muhammad Arif, and K. Kanchanadevi. “Plant disease detection using deep convolutional neural network.” Applied Sciences 12, no. 14 (2022): 6982. [CrossRef] [Google Scholar]
  7. Pradhan, Shradha S., and Rupali Patil. “Comparison of deep learning approaches for plant disease detection.” In Proceedings of International Conference on Wireless Communication: ICWiCOM 2019, pp. 575–583. Springer Singapore, 2020. [Google Scholar]
  8. Roy, Arunabha M., and Jayabrata Bhaduri. “A deep learning enabled multiclass plant disease detection model based on computer vision.” Ai 2, no. 3 (2021): 413–428. [CrossRef] [Google Scholar]
  9. Tiwari, Divyansh, Mritunjay Ashish, Nitish Gangwar, Abhishek Sharma, Suhanshu Patel, and Suyash Bhardwaj. “Potato leaf diseases detection using deep learning.” In 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 461–466. IEEE, 2020. [Google Scholar]
  10. Mahum, Rabbia, Haris Munir, Zaib-Un-Nisa Mughal, Muhammad Awais, Falak Sher Khan, Muhammad Saqlain, Saipunidzam Mahamad, and Iskander Tlili. “A novel framework for potato leaf disease detection using an efficient deep learning model.” Human and Ecological Risk Assessment: An International Journal (2022): 1–24. [Google Scholar]
  11. Rashid, Javed, Imran Khan, Ghulam Ali, Sultan H. Almotiri, Mohammed A. AlGhamdi, and Khalid Masood. “Multi-level deep learning model for potato leaf disease recognition.” Electronics 10, no. 17 (2021): 2064. [CrossRef] [Google Scholar]
  12. Kumar, Akshay, and M. Vani, “Image based tomato leaf disease detection.” In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1–6. IEEE, 2019. [Google Scholar]
  13. Agarwal, Mohit, Abhishek Singh, Siddhartha Arjaria, Amit Sinha, and Suneet Gupta. “ToLeD: Tomato leaf disease detection using convolution neural network.” Procedia Computer Science 167 (2020): 293–301. [CrossRef] [Google Scholar]
  14. Thangaraj, Rajasekaran, S. Anandamurugan, P. Pandiyan, and Vishnu Kumar Kaliappan. “Artificial intelligence in tomato leaf disease detection: a comprehensive review and discussion.” Journal of Plant Diseases and Protection 129, no. 3 (2022): 469–488. [CrossRef] [Google Scholar]
  15. Mukhopadhyay, Somnath, Munti Paul, Ramen Pal, and Debashis De. “Tea leaf disease detection using multi-objective image segmentation.” Multimedia Tools and Applications 80 (2021): 753–771. [CrossRef] [Google Scholar]
  16. Gayathri, S., D.C. Joy Winnie Wise, P. Baby Shamini, and N. Muthukumaran. “Image analysis and detection of tea leaf disease using deep learning.” In 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 398–403. IEEE, 2020. [CrossRef] [Google Scholar]
  17. Bao, Wenxia, Tao Fan, Gensheng Hu, Dong Liang, and Haidong Li. “Detection and identification of tea leaf diseases based on AX-RetinaNet.” Scientific Reports 12, no. 1 (2022): 2183. [CrossRef] [Google Scholar]
  18. Panigrahi, Kshyanaprava Panda, Himansu Das, Abhaya Kumar Sahoo, and Suresh Chandra Moharana. “Maize leaf disease detection and classification using machine learning algorithms.” In Progress in Computing, Analytics and Networking: Proceedings of ICCAN 2019, pp. 659–669. Springer Singapore, 2020. [CrossRef] [Google Scholar]
  19. Akanksha, Eisha, Neeraj Sharma, and Kamal Gulati. “OPNN: optimized probabilistic neural network based automatic detection of maize plant disease detection.” In 2021 6th international conference on inventive computation technologies (ICICT), pp. 1322–1328. IEEE, 2021. [CrossRef] [Google Scholar]
  20. Li, Ying, Shiyu Sun, Changshe Zhang, Guangsong Yang, and Qiubo Ye. “One-stage disease detection method for maize leaf based on multi-scale feature fusion.” Applied Sciences 12, no. 16 (2022): 7960. [CrossRef] [Google Scholar]
  21. Manavalan, R. “Towards an intelligent approaches for cotton diseases detection: A review.” Computers and Electronics in Agriculture 200 (2022): 107255. [CrossRef] [Google Scholar]
  22. Kumbhar, Shantanu, A. Nilawar, S. Patil, Bodireddy Mahalakshmi, and Manasi Nipane. “Farmer buddy-web based cotton leaf disease detection using CNN.” Int. J. Appl. Eng. Res 14, no. 11 (2019): 2662–2666. [Google Scholar]
  23. Khan, Asif Iqbal, S.M.K. Quadri, Saba Banday, and Junaid Latief Shah. “Deep diagnosis: A real-time apple leaf disease detection system based on deep learning.” Computers and Electronics in Agriculture 198 (2022): 107093. [CrossRef] [Google Scholar]
  24. Bansal, Prakhar, Rahul Kumar, and Somesh Kumar. “Disease detection in apple leaves using deep convolutional neural network.” Agriculture 11, no. 7 (2021): 617. [CrossRef] [Google Scholar]
  25. Baranwal, Saraansh, Siddhant Khandelwal, and Anuja Arora. “Deep learning convolutional neural network for apple leaves disease detection.” In Proceedings of international conference on sustainable computing in science, technology and management (SUSCOM), Amity University Rajasthan, Jaipur-India. 2019. [Google Scholar]
  26. Matin, Md Mafiul Hasan, Amina Khatun, Md Golam Moazzam, and Mohammad Shorif Uddin. “An efficient disease detection technique of rice leaf using AlexNet.” Journal of Computer and Communications 8, no. 12 (2020): 49. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.