Faculty Publications
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Item Quantum-inspired Arecanut X-ray image classification using transfer learning(John Wiley and Sons Inc, 2024) Naik, P.; Rudra, B.Arecanut X-ray images accurately represent their internal structure. A comparative analysis of transfer learning-based classification, employing both a traditional convolutional neural network (CNN) and an advanced quantum convolutional neural network (QCNN) approach is conducted. The investigation explores various transfer learning models with different sizes to identify the most suitable one for achieving enhanced accuracy. The Shufflenet model with a scale factor of 2.0 attains the highest classification accuracy of 97.72% using the QCNN approach, with a model size of 28.40 MB. Out of the 12 transfer learning models tested, 9 exhibit improved classification accuracy when using QCNN models compared to the traditional CNN-based transfer learning approach. Consequently, the exploration of CNN and QCNN-based classification reveals that QCNN outperforms traditional CNN models in accuracy within the transfer learning framework. Further experiments with qubits suggest that utilising 4 qubits is optimal for classification operations in this context. © 2024 The Author(s). IET Quantum Communication published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.Item Framework for Lightweight Deep Learning Model Using YOLOv5 for Arecanut Grade Assessment(Springer, 2024) Naik, P.; Rudra, B.Arecanut grading using Machine Learning (ML) approaches can be less ideal in real-world scenarios that require real-time operations due to the need for rapid processing and adaptation to varying conditions. This paper proposes an efficient and lightweight deep learning model for real-time Arecanut detection suitable for industrial applications. We introduce a novel Arecanut image dataset and explore the YOLOv5 architecture to achieve a lightweight and efficient model for both grading and detection. Our investigation reveals that a hybrid model utilizing GhostNet in Darknet-53 convolutions and a Feature Pyramid Network (FPN) in the YOLOv5 neck structure achieves optimal performance. This model achieves the best mean Average Precision (mAP) of 98.85% while maintaining a compact model size of only 1.9 MB. We compared our design with the latest YOLO models, and ours performed better in detecting Arecanuts. This accurate and cost-effective solution empowers Arecanut producers with the ability to identify top-grade Arecanuts, leading to economic benefits and mitigating health concerns associated with manual grading processes. Finally, we propose a framework for integrating this model into the agro-industry for future real-world implementation. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.