CURRENT CHALLENGES, AND FUTURE OPPORTUNITIES FOR FERMENTED TEA LEAF SEGMENTATION, CLASSIFICATION, AND OPTIMIZATION
DOI:
https://doi.org/10.29121/shodhkosh.v5.i1.2024.3949Keywords:
Fermented Tea Leaves, Image Segmentation, Classification Algorithms, Quality Optimization, Deep Learning, Hyperspectral Imaging, Agricultural AutomationAbstract [English]
Fermented tea leaves emerged as a significant agricultural commodity on the global scene. This type of product experiences segmentation, classification, and optimization due to the different textures, different stages of fermentation, and environmental influences. The article reviews the progresses and limitations made by automatic systems in the realm of image-based analysis of fermented tea leaves, machine learning algorithms, and optimization methods. The challenges of high segmentation accuracy in heterogeneous samples, robust classification among diverse tea varieties, and scaling of optimization strategies for quality enhancement are some key challenges. Apart from hybrid optimization algorithms designed to interpret the gap, future areas of opportunities that utilize deep learning and multimodal fusion. Highlights from different hyperspectral imaging approaches and AI-driven models providing quick solutions with high accuracy and cost-effectiveness.
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Copyright (c) 2024 C M Sulaikha, Dr. A SomaSundaram
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