Real-Time Plant Disease Prediction Using XGBoost and IoT-Enabled Smart Agriculture System

DOI

10.2991/978-94-6463-858-5_12How to use a DOI?

Keywords

Plant Disease Prediction; XGBoost; IoT Sensors; Raspberry Pi; Smart Agriculture; Real-Time Monitoring

Abstract

Plant disease prediction is a crucial aspect of modern precision agriculture, helping farmers detect and mitigate crop infections before significant yield losses occur. The proposed system leverages XGBoost-based machine learning integrated with IoT sensors and Raspberry Pi to provide an efficient, real-time, and cost-effective solution for disease prediction. Unlike traditional methods, which rely on manual observation and expert analysis, or deep learning approaches that demand high computational power, the proposed system balances accuracy, speed, and hardware efficiency for real-world agricultural deployment. The system extracts real-time environmental parameters such as temperature, humidity, soil moisture, and leaf health indicators using IoT sensors. The XGBoost model processes this sensor data, performing feature selection and disease classification with high accuracy (85-95%). Compared to deep learning methods, which require large datasets and expensive computing resources, the XGBoost-based approach offers fast, scalable, and low-power processing suitable for edge computing on Raspberry Pi. The system is also designed to be cost-effective (96%) and energy-efficient (90%), making it accessible to small-scale farmers. Additionally, a mobile application is integrated for real-time monitoring and alert notifications, allowing farmers to make informed decisions based on live data. The proposed system achieves 100% scalability, real-time feasibility, and adaptability to different crops, making it an ideal solution for smart agriculture. By combining IoT, machine learning, and edge computing, this system provides a highly efficient, automated, and data-driven approach to plant disease prediction, ensuring sustainable and improved crop management.

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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TY  - CONF
AU  - M. Saritha
AU  - R. Mohamed Irshath
AU  - S. Sanjay
AU  - A. Vetri Selvan
AU  - M. Sheik Abdullah
PY  - 2025
DA  - 2025/11/04
TI  - Real-Time Plant Disease Prediction Using XGBoost and IoT-Enabled Smart Agriculture System
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 120
EP  - 131
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_12
DO  - 10.2991/978-94-6463-858-5_12
ID  - Saritha2025
ER  -