Automated fruit classification plays a central role in post-harvest quality assessment, particularly in reducing dependence on manual inspection within smart agriculture systems. In this work, a deep learning framework, MSA-TNet, is introduced to address jujube fruit classification across two related tasks, binary br...
DFruitNet is proposed, a lightweight dual-attention-based convolutional neural network for dragon fruit disease classification and severity estimation that integrates channel and spatial attention mechanisms with multiscale feature fusion to effectively capture both local and global visual patterns.
Basab Nath, Sandeep Kumar, Yonis Gulzar et al.· Applied Fruit Science· 0 citations
Human Activity Recognition (HAR) systems using deep learning have shown significant promise; however, deploying such models on edge computing systems remains challenging due to constraints in inference latency, memory footprints and computational capacity. This study proposes a lightweight, end-to-end patch-based Trans...
This study benchmarks deep learning and machine learning approaches for classifying pediatric dental views using a publicly available dataset of 9,562 intraoral images from children aged 1-14 years, covering eight maxillary and mandibular view classes and highlights the potential of explainable, mobile-based AI systems...
E. Yasin, M. Koklu, Mohannad Alkanan et al.· BMC Oral Health· 0 citations
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