Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)

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A Smart IoT Enabled System for Leaf Disease Detection with Severity and Pesticide Recommendation

Authors
D. Aswani1, *, K. Ram Kumar1, Y. T. R. Shinee1, K. Lalith Akash1, A. Balu Karthik1
1Department of Computer Science & Engineering, Anil Neerukonda Institute of Technology and Sciences, Sangivalasa, Visakhapatnam, 531162, Andhra Pradesh, India
*Corresponding author. Email: ashwani.cse@anits.edu.in
Corresponding Author
D. Aswani
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_287How to use a DOI?
Keywords
ESP8266; ESP32; CNN; ResNet50; Efficient Net; Precision Agriculture; IoT; Deep Learning
Abstract

Monitoring the health of plants and diagnosing diseases at early stages is an important step to boost agricultural productivity. This project proposes an IoT plant monitoring system that collects images of plants, taking into consideration secondary environmental factors such as soil moisture, temperature, and humidity. This system majorly consists of ESP8266, ESP32, soil moisture sensor, and various other components that would still take the real-time data. The classification of images would involve the training of deep learning models, namely CNN, ResNet50, and Efficient Net to check for disease severity. Efficient classification of infected plants as well as image processing takes help from computer vision techniques. With the help of Gemini AI, suitable pesticides and control measures are recommended based on severity analysis. The approach would enable automated, data-driven decision- making for farmers to reduce reliance on manual inspection and improve crop health management. Utilizing IoT and AI, it further adds precision in agriculture that thereby enlightens resources utilization and practices of sustainable farming.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
Series
Advances in Computer Science Research
Publication Date
4 November 2025
ISBN
978-94-6463-858-5
ISSN
2352-538X
DOI
10.2991/978-94-6463-858-5_287How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - D. Aswani
AU  - K. Ram Kumar
AU  - Y. T. R. Shinee
AU  - K. Lalith Akash
AU  - A. Balu Karthik
PY  - 2025
DA  - 2025/11/04
TI  - A Smart IoT Enabled System for Leaf Disease Detection with Severity and Pesticide Recommendation
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 3425
EP  - 3439
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_287
DO  - 10.2991/978-94-6463-858-5_287
ID  - Aswani2025
ER  -