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: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
Infrared (IR) spectroscopy is widely used for chemical sensing, but extracting reliable chemical information from spectra remains challenging. Conventional interpretation is labor-intensive, relies on prior knowledge and reference spectra, and is difficult to scale, whereas most machine-learning methods are tailored to individual tasks or datasets, require large labeled training sets, and transfer poorly across analytical objectives and experimental datasets.
arXiv:2606. 13236v1 Announce Type: cross Abstract: Passive acoustic monitoring holds great promise for ecological inference, yet existing automated tools are typically narrowly trained and non-transferable.
By Olga Isupova, Danil Kuzin, Ella Browning, Tom Mills, Steven Reece
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
The paper introduces RNN Maxent, a new extension of the Maxent species distribution modelling framework that replaces its fixed linear feature dictionary with a neural network—specifically a Gated Recurrent Unit (GRU)—trained end‑to‑end via backpropagation. This approach retains Maxent’s presence‑only statistical foundations, background normalisation, and probability calibration while learning nonlinearity directly from data. Applied to Desert Locust distribution modelling using 50‑day environmental time‑series, RNN Maxent outperforms standard Maxent, achieving higher ROC AUC (0.862 vs. 0.792) and F1 scores (0.671 vs. 0.590).
By Alessandro Grassi, Edoardo Kimani Bellotto, Wassim El Azami, Sabrina Outmani, Maximilien Houel