IoT-Enabled Real-Time Monitoring and Machine Learning-Based Prediction System for Optimizing Tea Withering Process

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.