ArticleFood science & nutrition2022
Moisture contents and product quality prediction of Pu-erh tea in sun-drying process with image information and environmental parameters.
Article in Food science & nutrition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Digital Sensing for Comprehensive Tea Quality Evaluation: From Dry Tea to Tea Infusion and Infused Leaves.Comprehensive reviews in food science and food safety · 2026Review
- Computer Vision from Tea Cultivation to Quality Evaluation.Foods (Basel, Switzerland) · 2026Review
- Predicting Corn Moisture Content in Continuous Drying Systems Using LSTM Neural Networks.Foods (Basel, Switzerland) · 2025Article
- GNN-FTuckER: A novel link prediction model for identifying suitable populations for tea varieties.PloS one · 2025Article
- Rapid Color Quality Evaluation of Needle-Shaped Green Tea Using Computer Vision System and Machine Learning Models.Foods (Basel, Switzerland) · 2024Article
- Moisture contents and product quality prediction of Pu-erh tea in sun-drying process with image information and environmental parameters.Food science & nutrition · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
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Abstract
In this study, moisture contents and product quality of Pu-erh tea were predicted with deep learning-based methods. Images were captured continuously in the sun-drying process. Environmental parameters (EP) of air humidity, air temperature, global radiation, wind speed, and ultraviolet radiation were collected with a portable meteorological station. Sensory scores of aroma, flavor, liquor color, residue, and total scores were given by a trained panel. Convolutional neural network (CNN) and gated recurrent unit (GRU) models were constructed based on image information and EP, which were selected in advance using the neighborhood component analysis (NCA) algorithm. The evolved models based on deep-learning methods achieved satisfactory results, with RMSE of 0.4332, 0.2669, 0.7508 (also with
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