Hugging Face Trending Papers August 17, 2026

Turning spectra into images improves plant trait retrieval with 2D-CNNs

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Hyperspectral reflectance spectroscopy enables non-destructive estimation of plant functional traits, yet current deep learning approaches process spectra as one-dimensional sequences, which limits how they capture long-range inter-band dependencies. We asked whether transforming 1D spectra into 2D image representations improves multi-trait prediction with convolutional neural networks (CNN).

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arXiv Machine Learning

1d ago

Turning spectra into images improves plant trait retrieval with 2D-CNNs

arXiv:2608. 16661v1 Announce Type: cross Abstract: Hyperspectral reflectance spectroscopy enables non-destructive estimation of plant functional traits, yet current deep learning approaches process spectra as one-dimensional sequences, which limits how they capture long-range inter-band dependencies.

By Javier Lopatin, Teja Kattenborn, Eya Cherif, Sebasti\'an Moreno

fine-tuning benchmarks

arXiv Machine Learning

Jun 2

Leaf Spectral Reflectance Prediction Using Multi-Head Attention Neural Networks

arXiv:2606. 01432v1 Announce Type: new Abstract: Accurate modeling of leaf spectral reflectance from physiological and biochemical traits is essential for advancing remote sensing applications in plant science and precision agriculture.

By Parastoo Farajpoor, Alireza Pourreza, Mohammadreza Narimani, Ashraf El-Kereamy, Matthew W. Fidelibus

arXiv Machine Learning

Jun 26

Batch-Invariant Spectral Intelligence for Robust and Explainable Insect Authentication

arXiv:2606. 26757v1 Announce Type: new Abstract: Edible insects offer an efficient source of alternative protein, requiring less land, water and emitting less greenhouse gas than conventional livestock.

By Majharulislam Babor, Giacomo Rossi, Annalisa Altavilla, Oliver Schl\"uter, Marina M. -C. H\"ohne

fine-tuning safety

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 Machine Learning

Aug 4

Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction

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

computer-vision benchmarks

Hugging Face Trending Papers

Aug 2

Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction

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.

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