Hyperspectral imaging (HSI) offers nondestructive assessment of fish freshness by detecting biochemical alterations across spectral bands. However, conventional deep learning approaches do not fully address the particular characteristics of HSI data, such as spectral dominance over spatial textures, ordinal label structure, and a small number of training samples.
arXiv:2608. 12227v1 Announce Type: cross Abstract: Hyperspectral imaging (HSI) offers nondestructive assessment of fish freshness by detecting biochemical alterations across spectral bands.
By Kazi Nabiul Alam, Pooneh Bagheri Zadeh, Akbar Sheikh-Akbari
arXiv:2608. 01202v1 Announce Type: cross Abstract: Fruit ripeness prediction (FRP) is a classification-based agricultural computer vision task that has attracted much attention, thanks to its wide-ranging advantages in agriculture field for both pre-harvest and post-harvest management.
By Ahmed Baha Ben Jmaa, Faten Chaieb, Anna Fabija\'nska
arXiv:2606. 00080v1 Announce Type: cross Abstract: Marine plankton underpin aquatic food webs and play a key role in global CO2 sequestration, making reliable species identification critical for understanding ocean health and climate feedbacks.
By Alan Gerson Contreras Montanares, Luis Valenzuela, Luis Mart\'i, Nayat Sanchez-Pi
Fruit ripeness prediction (FRP) is a classification-based agricultural computer vision task that has attracted much attention, thanks to its wide-ranging advantages in agriculture field for both pre-harvest and post-harvest management. Accurate and timely FRP can be achieved using machine/deep learning-based hyperspectral image classification techniques.
arXiv:2607. 16514v1 Announce Type: cross Abstract: Image-based dietary assessment promises to replace costly, bias-prone manual recalls, but portion estimation remains a major blocker.
By Lin Liao, Peng Li
arXiv:2509. 04682v2 Announce Type: replace-cross Abstract: Deploying reliable bioacoustic monitoring systems requires models that generalize under high-noise, low-SNR conditions and evaluation protocols that expose deployment-relevant failure modes, gaps largely unaddressed in current UPAM practice.
By Nicholas R. Rasmussen, Rodrigue Rizk, Longwei Wang, KC Santosh
arXiv:2608. 03428v1 Announce Type: cross Abstract: Image based dietary assessment offers a scalable alternative to self reported food diaries, yet fine-grained food recognition remains challenging due to high intra-class variability and visually similar dishes.
By Dimitrios I. Zaridis, Traianos Tsiokris, Vasileios C. Pezoulas, Daphni Plati, Eugenia Mylona, Eleni Georga, Nikos Tsiknakis, Antonis Sakellarios, Dimitrios I. Fotiadis
arXiv:2607. 08423v1 Announce Type: new Abstract: The rapid integration of Large Vision-Language Models (VLMs) into critical infrastructure promises to revolutionize personalized healthcare and dietary management.
By Qian Jiang, Zhecheng Shi, Jingpu Yang, Zirui Song, Miao Fang
arXiv:2606. 25989v1 Announce Type: cross Abstract: Automated classification of marine species from underwater imagery is essential for scalable ocean biodiversity monitoring and conservation policy.
By Dan Zimmerman, Dimitris A. Pados, George Sklivanitis
arXiv:2606. 13302v1 Announce Type: new Abstract: Wave parameters in the nearshore are crucial for coastal engineering, shoreline protection, marine hazard assessment, and coastal management for climate resilience.
By Abubakar Hamisu Kamagata, Dharm Singh Jat, Attlee Munyaradzi Gamundani, Abhishek Srivastava, Paramasivam Saravanakumar
arXiv:2608. 17561v1 Announce Type: cross Abstract: The health of marine ecosystems is a critical indicator of global environmental change, yet the physical constraints of underwater observation and the intrinsic challenges of processing marine imagery severely limit the scalability of systematic monitoring.
By Cesar Borja, Breck A. McCollum, Jarret E. Byrnes, Kenneth Sebens, Ana C. Murillo