Authors : Muthukumaran K.; Muneshwaran S.; Bhuvaneshwaran G.; Iyyappan A.; Jalum Dhinesh

Volume/Issue : Volume 11 - 2026, Issue 3 - March

Google Scholar : https://tinyurl.com/3vbxyk66

Scribd : https://tinyurl.com/4myyb6n8

DOI : https://doi.org/10.38124/ijisrt/26mar1561

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Early and accurate diagnosis of plant diseases at different stages of farming is one of the major challenges faced by modern farmers today. Most of the time, they depended on manual checks or local advice, which are usually slow and uncertain. Diseases that go unidentified can develop into severe ones, resulting into crop damage, reduced yield, and economic strain on the farming family. Today's tools are separate-from- each-other in that they may identify a disease but fail to advise on the next steps to take by the farmer or prevent further infection. This usually causes a catch-22 for farmers without appropriate assistance at the most critical juncture. This is where Plantify comes to the scene. It is a selfexplanatory, AI- enabled app making management of plant health very easy and effective. The users only have to take a picture of the diseased leaf and Plantify will automatically identify the disease. It then prescribes the appropriate remedy and shows users where disease hotspots are standing using an online map via GPS data so that preventive actions can be taken before it's too late. With all those features in one platform, Plantify provides farmers with the confidence and backup they require to protect their crops, improve their harvests, and secure their livelihoods. By seamlessly integrating disease detection, expert guidance, and real-time monitoring into a single platform, Plantify converts conventional farming to data-driven precision agriculture. Timely knowledge is empowered in the hands of the farmers through this holistic way of delivering timely knowledge to improve overall performance yield quality and sustainable practice of agriculture toward a more resilient and profitable agricultural ecosystem.

Keywords : Artificial Intelligence, Plant Disease Detection, Precision Agriculture, Real-Time Alerts, Farmer Support.

References :

1. S. P. Mohanty, D. P. Hughes, and M. Salathé, "Using deep learning for image-based plant disease detection," Frontiers in Plant Science, vol. 7, p. 1419, Feb. 2016.
2. K. P. Ferentinos, "Deep learning models for plant disease detection and diagnosis," Computers and Electronics in Agriculture, vol. 145, pp. 311-318, 2018.
3. S. Sladojevic, M. Arsenovic, A. Anderla, D. Culibrk, and D. Stefanovic, "Deep learning For plant disease detection," Frontiers in Plant Science, vol. 7, p. 1419, Nov. 2016.
4. J. Zhang, Y. Wang, and Z. Ma, "Real-time plant disease detection using deep learning with mobile application development," in Proc. IEEE Int. Conf. on Smart Computing & Communications, Changsha, China, pp. 614-619, Dec. 2020.
5. D. Singh, S. Singh, and M. P. Singh, "OpenCV-based image preprocessing for enhancing plant disease detection accuracy," in Proc. IEEE Int. Conf. on Computer & Information Technology, Bangkok, Thailand, pp. 102-107, Oct. 2018.
6. A. S. Khan and M. Iqbal, "Integrated smart farming system for plant disease detection using IoT and deep learning," IEEE Internet of Things Journal, vol. 9, no. 4, pp. 3173-3183, Feb. 2022.
7. A. Kaur and V. Singh, "Deep learning-based approaches for plant disease detection and diagnosis: A survey," IEEE Access, vol. 9, pp. 134811-134833, Sep. 2021.
8. M. R. Islam, N. C. Saha, and A. Al-Emran, "A comprehensive review of AI-based plant disease detection systems: From traditional machine learning to deep learning," IEEE Transactions on Industrial Informatics, vol. 18, no. 1, pp. 627-638, Jan. 2022.
9. S. R. Dubey and S. R. Dubey, "Disease detection in potato plants using CNN-based transfer learning," in Proc. IEEE Int. Conf. on Computer Vision & Image Process., Pune, India, pp. 1-6, Sep. 2019.
10. P. Mohanty, D. P. Hughes, and M. Salathé, "Using deep learning for image-based plant disease detection," Frontiers in Plant Science, vol. 7, pp. 1-7, Feb. 2016. (duplicate/canonical ref for Mohanty et al.)
11. G. G. G. P. da Silva, A. H. P. de Souza, and M. G. da Silva, "Automated plant disease diagnosis via CNN with advanced image preprocessing," IEEE Latin America Transactions, vol. 19, no. 6, pp. 917-924, Jun. 2021.
12. F. Khan and A. K. Singh, "Disease detection in fruits and vegetables using transfer learning with DenseNet," in Proc. IEEE Int. Conf. on Computing, Communication & Engineering Sciences, Coimbatore, India, pp. 248-253, Aug. 2020.
13. H. Lu, J. Cao, and Y. Xiao, “An attention-based deep learning model for real-time tomato disease detection," IEEE Access, vol. 7, pp. 176140-176151, Dec. 2019.
14. H. Rahman, M. M. Uddin, and S. C. Das, "Design and implementation of a smartphone-based plant disease detection system,” in Proc. IEEE Int. Conf. on Comput. & Inf. Technol., Dhaka, Bangladesh, pp. 1–6, Dec. 2019.
15. S. Ma, T. Wang, and Z. Liu, “Deep residual learning for plant disease detection and diagnosis with small sample size," IEEE Access, vol. 9, pp. 102434- 102446, Jul. 2021.
16. Y. Zhang, Q. Song, and P. Cui, "Plant disease detection and classification using an improved DenseNet model," in Proc. IEEE Int. Conf. on Image, Vision & Signal Process., Chengdu, China, pp. 110-114, Jul. 2021.
17. K. P. Singh and A. K. Singh, "A deep learning framework for real-time agricultural pest and disease detection," IEEE Transactions on Industrial Applications, vol. 57, no. 4, pp. 4153-4161, Jul. 2021.
18. H. P. Singh and M. Kumar, "Deep convolutional neural networks for automated plant disease detection and diagnosis," IEEE Transactions on Agricultural Electronics, vol. 1, no. 1, pp. 25-34, Mar. 2020.
19. A. A. Al-Emran, "Survey of mobile applications for plant disease detection: Features and challenges," IEEE Access, vol. 9, pp. 78000-78010, Jun. 2021.
20. X. Li, Y. Zhang, and X. Liu, “A real-time plant disease warning system based on weather data and deep learning," IEEE Access, vol. 8, pp. 182345-182356, Oct.2020.