Umagandhi, R (2024) IoT-Enabled Real-Time Monitoring and Machine Learning-Based Prediction System for Optimizing Tea Withering Process. IoT-Enabled Real-Time Monitoring and Machine Learning-Based Prediction System for Optimizing Tea Withering Process, 31 (8). pp. 56-86. ISSN 1074-133X

[thumbnail of IoT-Enabled Real-Time Monitoring and Machine Learning-Based Prediction System for Optimizing Tea Withering Process.pdf] Text
IoT-Enabled Real-Time Monitoring and Machine Learning-Based Prediction System for Optimizing Tea Withering Process.pdf - Published Version
Download (827kB)

Abstract

The withering process in tea production is critical for ensuring high-quality tea but
traditionally relies on manual monitoring, leading to inconsistencies. This paper presents
an IoT-based real-time monitoring system integrated with machine learning to optimize the
withering process. Using ThingSpeak as the IoT dashboard, the system gathers real-time
environmental data such as temperature, humidity, and moisture content. Machine learning
models, XGBoost and LightGBM, predict moisture content based on this data, enabling
proactive adjustments. XGBoost achieved a Mean Absolute Error (MAE) of 0.99 and an
R-squared (R²) value of 0.92, demonstrating high predictive accuracy. The system’s real
time insights help tea producers optimize the withering process, improving quality and
reducing waste. This research offers a scalable solution for both small and large tea
producers, showcasing the potential of IoT and machine learning in modernizing
agriculture.
Introduction: Tea production relies heavily on the withering process, traditionally
monitored manually, which can be inconsistent. By integrating IoT and machine learning,
real-time data is collected to predict moisture levels, offering more control. This research
uses XGBoost and LightGBM models, with ThingSpeak for data visualization, to reduce
errors and improve tea quality.
Objectives: This research aims to create a real-time IoT-based system that monitors and
controls environmental factors during tea withering. By predicting moisture levels using
XGBoost and LightGBM, the system improves tea quality and operational efficiency. It
also seeks to ensure scalability and reduce variability through automation and data-driven
insights.
Methods: DHT22 and moisture sensors connected to an ESP32 microcontroller collected
environmental data, sent to ThingSpeak for analysis. XGBoost and LightGBM models,
optimized with GridSearchCV, predicted moisture content. The system provided real-time
visualizations and alerts for better decision-making during tea withering.
Results: The system improved moisture control, with XGBoost achieving an MAE of 0.99
and LightGBM 1.01. Real-time monitoring via ThingSpeak enabled better decisions,
adapting well to environmental changes. The system proved scalable, benefiting both small
and industrial tea producers.
Conclusions: IoT and machine learning enhance tea withering by providing real-time
insights and predictive analytics. The ThingSpeak dashboard effectively visualizes data,
helping optimize tea production. Future improvements will focus on refining models and
enhancing the user interface for better scalability and usability.

Item Type: Article
Uncontrolled Keywords: IoT, Tea Processing, Machine Learning, Smart Agriculture, XGBoost
Divisions: PSG College of Arts and Science > Department of Carnatic Music
Depositing User: Dr. B Sivakumar
Date Deposited: 10 Dec 2025 05:44
Last Modified: 10 Dec 2025 05:44
URI: https://ir.psgcas.ac.in/id/eprint/2566

Actions (login required)

View Item
View Item