Browsing by Author "Muhtasim, Tasnuva"

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    Fruit Quality Classification using Deep Learning and Explainable AI
    (Daffodil International University, 2025-01-14) Annafi, Abdun Nafi; Muhtasim, Tasnuva
    Fruit quality classification is a critical task in agriculture and food industries to ensure standardization and market value. This study evaluates the performance of VGG19, MobileNetV2, ResNet50, custom CNN and BiLSTM for fruit classification using deep learning. A dataset of 3,758 images across seven fruit classes was used for training and evaluation. Among the tested models, MobileNetV2 achieved the highest accuracy (99.48%), making it the most suitable for real-world applications due to its efficiency. LIME (Local Interpretable Model-Agnostic Explanations) was employed to interpret model predictions, verifying that fruit characteristics like color, shape, and texture were key factors in classification decisions. The study highlights dataset imbalance and lighting variations as primary challenges. Future improvements include dataset expansion, hyperparameter optimization, and real-time deployment of the best-performing model. This research provides insights into selecting optimal deep learning models for automated fruit classification, contributing to precision agriculture and quality assurance in food industries.
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    Fruit Quality Classification using Deep Learning and Explainable AI
    (Daffodil International University, 2025-05-14) Annafi, Abdun Nafi; Muhtasim, Tasnuva
    Fruit quality classification is a critical task in agriculture and food industries to ensure standardization and market value. This study evaluates the performance of VGG19, MobileNetV2, ResNet50, custom CNN and BiLSTM for fruit classification using deep learning. A dataset of 3,758 images across seven fruit classes was used for training and evaluation. Among the tested models, MobileNetV2 achieved the highest accuracy (99.48%), making it the most suitable for real-world applications due to its efficiency. LIME (Local Interpretable Model-Agnostic Explanations) was employed to interpret model predictions, verifying that fruit characteristics like color, shape, and texture were key factors in classification decisions. The study highlights dataset imbalance and lighting variations as primary challenges. Future improvements include dataset expansion, hyperparameter optimization, and real-time deployment of the best-performing model. This research provides insights into selecting optimal deep learning models for automated fruit classification, contributing to precision agriculture and quality assurance in food industries