ArticleNPJ science of food2025
Convolutional neural network based on transfer learning for discriminating the fermentation degree of black tea.
Article in NPJ science of food, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- RTD-YOLO: a precise quality detection and grading model for rose tea.NPJ science of food · 2026Article
- Classification of Different Fermentation Stages of Black Tea Using a Lightweight CNN Optimized by Knowledge Distillation.Foods (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Black tea is among the most widely consumed tea. The fermentation process is crucial for developing the flavor of black tea. Currently, many producers rely on personal experience to gauge fermentation, which can be inconsistent and subjective. Additionally, large models are impractical for use in production. Based on this, this paper introduces a lightweight convolutional neural network utilizing transfer learning to assess the fermentation level of black tea. Initially, we applied a model-based transfer learning strategy and conducted pre-training weight experiments to compare and select the student model and the teacher model. Next, we modified the loss function with PolyLoss and optimizer with AdamW for the student model. Finally, we performed a knowledge distillation experiment on the student model. Results indicated that the improved model's accuracy, precision, recall, and F1 improved by 0.0415, 0.0215, 0.0902, and 0.0645, respectively. This research offers technical assistance for digital production of black tea.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.