arXiv Machine Learning

Radio Frequency Detection and Classification of Microplastics in Water

The study introduces a machine learning–assisted radio‑frequency dielectric spectroscopic cytometry platform for detecting and classifying micro‑plastic particles in water. Eight types of 10 µm nominal‑diameter micro‑plastics were distinguished in deionized water using RF scattering parameters measured at 0.2–9 GHz, achieving macro‑average F1‑scores, precision, and recall above 0.71. The method also maintained PET classification performance in saline media up to 6.6 % sea salt, demonstrating feasibility for rapid, single‑particle micro‑plastic identification in aqueous environments.

arXiv Machine Learning

Sep 1

MWIR-4-Plastic: The Identification of Complex End-of-Life Industrial Plastic using Mid-wave Infrared Hyperspectral Imaging and Machine Learning

The paper presents the first publicly available hyperspectral imaging (HSI) dataset of shredded black plastics from end‑of‑life vehicle waste, covering four industrial polymers across RGB, VNIR, SWIR, and MWIR modalities. It introduces a multi‑modal spectral‑spatial framework that combines foreground isolation, pixel‑wise classification, and object‑level majority voting, leveraging hyperspectral transformers and chemometric band selection to accurately classify complex black plastics. The study benchmarks nine processing methods—including chemometric, machine learning, and deep learning architectures—providing a reproducible, comprehensive benchmark for industrial hyperspectral object analysis.

By Elias Arbash, Andr\'ea de Lima Ribeiro, Filipa Sim\~oes, Ahmed Jamal Afifi, Aldino Rizaldy, Yuleika Madriz, Samuel Thiele, Sandra Lorenz, Margret Fuchs, Pedram Ghamisi, Paul Scheunders, Richard Gloaguen

llms computer-vision multimodal benchmarks

arXiv Machine Learning

Aug 4

Retrieval of Coastal Biogeochemical Parameters From Near-Surface Hyperspectral Remote Sensing Reflectance Using Physics-Aware Meta-Learning

arXiv:2605. 05623v3 Announce Type: replace Abstract: Hyperspectral in situ sensing has shown promise in retrieving aquatic biogeochemical (BGC) parameters, such as total suspended solids, dissolved organic carbon, and total chlorophyll-a, for cost-effective monitoring of coastal water quality.

By Yiqing Guo, Nagur R. C. Cherukuru, Eric A. Lehmann, S. L. Kesav Unnithan, Tim J. Malthus, Gemma Kerrisk, Xiubin Qi, Faisal Islam, Tisham Dhar, Mark J. Doubell

fine-tuning benchmarks

arXiv Machine Learning

Jul 31

Region-adaptable retrieval of coastal biogeochemical parameters from near-surface hyperspectral remote sensing reflectance using physics-aware meta-learning

arXiv:2605. 05623v2 Announce Type: replace Abstract: Hyperspectral in situ sensing has shown promise in retrieving aquatic biogeochemical (BGC) parameters, such as total suspended solids, dissolved organic carbon, and total chlorophyll-a, for cost-effective monitoring of coastal water quality.

By Yiqing Guo, Nagur R. C. Cherukuru, Eric A. Lehmann, S. L. Kesav Unnithan, Tim J. Malthus, Gemma Kerrisk, Xiubin Qi, Faisal Islam, Tisham Dhar, Mark J. Doubell

fine-tuning benchmarks

arXiv AI

Jun 11

FOCUS on Contamination: Hydrology-Informed Noise-Aware Learning for Geospatial PFAS Mapping

arXiv:2502. 14894v5 Announce Type: replace-cross Abstract: Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with significant public health impacts, yet large-scale monitoring remains severely limited due to the high cost and logistical challenges of field sampling.

By Jowaria Khan, Alexa Friedman, Sydney Evans, Rachel Klein, Runzi Wang, Katherine E. Manz, Kaley Beins, David Q. Andrews, Elizabeth Bondi-Kelly

computer-vision

arXiv Machine Learning

Aug 4

From field-scale to large-scale spectral libraries: Tabular foundation models in soil spectroscopy

arXiv:2608. 00608v1 Announce Type: new Abstract: Visible and near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy enable rapid, cost-effective prediction of soil properties.

By Viacheslav Barkov, Jonas Schmidinger, Robin Gebbers, Martin Atzmueller

llms benchmarks

arXiv AI

Jul 23

Physics-Aware Complex-Valued State Space Model with Scattering-Prior Feature Modulation for PolSAR Image Classification

arXiv:2607. 19787v1 Announce Type: cross Abstract: Polarimetric synthetic aperture radar (PolSAR) image classification is a representative task for physics-aware GeoAI, where land-cover semantics are closely coupled with electromagnetic scattering mechanisms.

By Fangyan Zhang, Fan Zhang, Shiqi Zhou, Jun Ni, Carlos L\'opez-Mart\'inez, Qiang Yin

rag computer-vision benchmarks

arXiv Machine Learning

Aug 21

The impact of feature engineering and an optimisation framework for ocean colour machine learning

arXiv:2608. 19899v1 Announce Type: cross Abstract: Machine learning (ML) is widely used for the development of ocean colour algorithms, but most studies focus on model parameter training and hyperparameter tuning.

By Edson Silva, Julien Brajard, Simon Cappe, Lasse H. Pettersson, Fran\c{c}ois Counillon

arXiv AI

Aug 14

Simulation-to-real transfer learning for infrared spectroscopic chemical sensing and analysis from molecules to complex samples

arXiv:2608. 13341v1 Announce Type: cross Abstract: Infrared (IR) spectroscopy is widely used for chemical sensing, but extracting reliable chemical information from spectra remains challenging.

By Yusen Tan, Yixuan Chen, Zheng Fang, Pan Liu, Yifan Li, Qinyu Guo, Zhedong Lin, Yuqiang Li, Xiangxiang Zeng, Tong Wang, Jun Xia

fine-tuning safety

arXiv Machine Learning

Sep 22

Robust small-molecule identification from incomplete, degraded, and inconsistent spectra using multimodal mixed-condition training

arXiv:2609.14360v2 Announce Type: replace Abstract: Reliable small-molecule identification often requires complementary evidence from multiple spectroscopic measurements. In practice, however, spectr...

By Bowen Gao, Lei Zhu, Yiying Wang, Wenjie Yu

multimodal

arXiv Machine Learning

Jun 5

A differentiable machine learning small-angle X-ray scattering analysis framework for structure elucidation of lipid nanoparticles

arXiv:2606. 05200v1 Announce Type: cross Abstract: Lipid nanoparticles (LNPs) are efficient delivery systems for negatively charged nucleic acids.

By Maria B{\aa}nkestad, Sandra Barman, Magnus R\"oding, Erik Kaunisto, Viktoriia Meklesh, Audrey Gallud, Marco Mendez, Marianna Yanez Arteta, Stefan Norberg, Ann Terry, Smita Chakraborty, Shun Yu, Jerk R\"onnols, Sepideh Pashami

arXiv Machine Learning

Aug 14

A Multispectral Framework for the Detection of Calcium Carbide-Induced Ripening and Shelf-Life Estimation in Climacteric Fruits

arXiv:2608. 13073v1 Announce Type: new Abstract: Significant health risks are associated with the illegal, yet commonly practiced use of industrial-grade Calcium Carbide (CaC2) for ripening climacteric fruits like mango and banana, which leaves behind trace residues of arsenic and phosphorus.

By Gurbhit Chaurakoti, Harshit Kumar, Hani Kumar, Anurag Singh, Ram Asrey

arXiv Machine Learning

Aug 11

Integrating spectral and morphological plant features with decision-tree models for early-season cotton biomass and nitrogen status estimation from multi-year UAV data

arXiv:2608. 07801v1 Announce Type: cross Abstract: Precision nitrogen (N) management (PNM) for cotton requires in-season monitoring of crop growth parameters and N status indicators to decide fertilizer timing, placement, and application rates for optimal canopy development and yield.

By Vaishali Swaminathan, Nithya Rajan, J Alex Thomasson, Amrit Shrestha, Karem Meza Capcha, Robert Hardin, Pramod Pokhrel

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