IJSMT Journal

International Journal of Science, Strategic Management and Technology

An International, Peer-Reviewed, Open Access Scholarly Journal Indexed in recognized academic databases · DOI via Crossref The journal adheres to established scholarly publishing, peer-review, and research ethics guidelines set by the UGC

ISSN: 3108-1762 (Online)
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AI-SMART PLANT HEALTH MONITORING SYSTEM USING DEEP LEARNING

AUTHORS:
Abitha E
Sruthi Jayaram J
Akesh S
Mentor
Dr. R. Parameswari
Affiliation
Department of Computer Science and Information Technology, School of Computing Sciences
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract

The increasing demand for food production has made efficient plant health monitoring a critical aspect of modern agriculture. Traditional methods rely on manual inspection, which is time-consuming and often inaccurate. This paper proposes an AI-based smart plant health monitoring system that integrates Internet of Things (IoT) sensors with deep learning techniques to enable real-time monitoring and early detection of plant diseases. Environmental parameters such as temperature, humidity, and soil moisture are continuously collected using IoT devices, while leaf images are analyzed using a Convolutional Neural Network (CNN) model for accurate disease classification

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E, A., J, S. J. & S, A. (2026). AI-Smart Plant Health Monitoring System using Deep Learning. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.019

E, Abitha, et al.. "AI-Smart Plant Health Monitoring System using Deep Learning." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.019.

E, Abitha,Sruthi J, and Akesh S. "AI-Smart Plant Health Monitoring System using Deep Learning." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.019.

References
1.Fuentes, S. Yoon, S. C. Kim, and D. S. Park, “A robust deep-learning-based detector for real-time tomato plant diseases,” Sensors, vol. 17, no. 9, pp. 2022, 2017.

2.Liu, Y. Zhang, D. He, and Y. Li, “Identification of apple leaf diseases based on deep convolutional neural networks,” Agronomy, vol. 11, no. 1, pp. 93, 2021.

3.C. Too, L. Yujian, S. Njuki, and L. Yingchun, “A comparative study of fine-tuning deep learning models for plant disease identification,” Computers and Electronics in Agriculture, vol. 161, pp. 272–279, 2019.

4.Google, “Gemini API Documentation,” [Online]. Available: https://ai.google.dev/gemini-api/docs

5.Chen, J. Chen, D. Zhang, Y. Sun, and Y. A. Nanehkaran, “Using deep transfer learning for image-based plant disease identification,” Computers and Electronics in Agriculture, vol. 173, pp. 105393, 2020.

6.Su et al., “Spatio-temporal monitoring of wheat yellow rust using UAV multispectral imaging,” Computers and Electronics in Agriculture, vol. 162, pp. 103–112, 2019.

7.P. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Computers and Electronics in Agriculture, vol. 145, pp. 311–318, 2018.

8.H. Saleem, J. Potgieter, and K. M. Arif, “Plant disease detection and classification by deep learning,” Plants, vol. 8, no. 11, pp. 468, 2019.

9.PlantVillage Dataset, “PlantVillage Dataset,” [Online]. Available: https://plantvillage.psu.edu/

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. 1419, 2016.
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✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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