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

Batch-Invariant Spectral Intelligence for Robust and Explainable Insect Authentication

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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

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).

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

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

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Automated identification of Ichneumonoidea wasps via YOLO-based deep learning: Integrating HiresCam for Explainable AI

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