A Hybrid Decision Logic Framework Combining Fuzzy Systems and Machine Learning Classifier for Post-Fermentation Tea Quality Grading
Year: 2025
Type: article
Abstract: This study addresses the need for reliable post-fermentation tea quality grading by proposing a hybrid decision logic framework that com-bines narrowband spectrophotometric sensing, computer vision-based fermentation indicators, and intelligent classification techniques. The proposed methodology employs low-cost sensing and digital imaging for feature extraction, followed by a two-stage modeling approach that integrates rule-based fuzzy inference system (FIS) with machine learning (ML) classification. After feature extraction, statistical and ML-based feature selection techniques were applied to identify the most informative features. The initial stage employs a FIS to identify the grade and its confidence score based on recurrent fuzzy patterns corresponding to each tea grade. In the second stage, a light-gradient boosting machine LightGBM classifier is trained to enhance predictive accuracy and generate probabilistic confidence scores. Finally, hybrid decision logic is applied to finalize the grade. The proposed hybrid model achieved an accuracy of 99.00%, precision of 99.53%, recall of 98.27%, and F1-score of 98.74%, outperforming both traditional ML and state-of-the-art models. It demonstrated robust generalization across varying sample sizes and cross-validation folds. Ablation studies confirmed the efficiency of the FIS, showing strong performance with reduced feature sets. Furthermore, the framework maintained shorter execution times than ensemble-based methods, making it suitable for real-time and resource-constrained environments.
DOI: 10.14419/3s15f186
Authors: C M Sulaikha, A. Somasundaram
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4414259086