Tea Leaf Withering Schedule Prediction Using Spectral Moisture Sensing
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Solution Overview
Problem
The monitoring and control of the withering process in tea production is challenging due to its slow pace, inefficient moisture determination methods, difficulties in managing multiple withering troughs, and lack of real-time moisture level monitoring, which affects the quality of tea production.
Innovation Solution
A system and method utilizing image processing and machine learning models to analyze tea leaf images and ambient data for precise moisture estimation and withering schedule prediction, incorporating spectral data analysis, bounding box classification, and time series forecasting to generate and fine-tune withering schedules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If microwave oven is used for moisture determination, then measurement precision is improved, but loss of time increases and ease of operation deteriorates
Solution Approach 1:
The patent replaces the mechanical/thermal microwave drying method with an optical sensing system using spectral cameras and image processing algorithms. The system captures spectral images of tea leaves and uses machine learning models to estimate moisture content non-contactly and instantaneously, eliminating the time-consuming microwave drying process while maintaining measurement accuracy.
2Measurement precision
If multiple troughs are monitored using shared microwave oven, then measurement precision is improved, but productivity deteriorates and loss of time increases
Solution Approach 1:
The patent divides the monitoring system into multiple independent spectral camera units, each assigned to monitor specific withering troughs. This segmentation allows simultaneous monitoring of multiple troughs without requiring sequential use of a single shared microwave oven, thereby improving productivity and reducing time loss while maintaining measurement precision through dedicated optical sensors.
3Ease of operation
If traditional moisture checking method is used, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system enables self-service monitoring where the spectral camera automatically captures images of tea leaves and the integrated machine learning model instantly processes the images to determine moisture content. This eliminates the need for manual sampling and microwave drying operations, maintaining ease of operation through automation while achieving high measurement precision through advanced optical sensing and algorithms.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables real-time, efficient, and accurate monitoring of tea leaf moisture levels across multiple troughs, optimizing the withering process and enhancing tea quality by predicting optimal processing times.
Implementation Method 1
receiving a plurality of spectral images of a plurality of reference tea leaves captured using a spectral camera device
Data Source
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AI summary
As the withering process of tea leaves takes a long time to reach a desired moisture level, estimating when it is time to move to the next step to reach the target tea leaf moisture is difficult and inefficient. Method and system disclosed herein provide an approach for withering schedule prediction of tea leaves. The system, by performing a spectral data analysis on an image of a plurality of tea leaves, estimates the moisture percentage in the plurality of tea leaves, for a selected time stamp. Based on the predicted moisture level, a current temperature value, a current relative humidity value, and a current time stamp, the system generates a withering schedule for the plurality of tea leaves. The generated withering schedule is fine-tuned based on a course correction of an impact of deviation in one or more ambient parameters on the prediction of the withering schedule.