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: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: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
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
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:2509. 06625v3 Announce Type: replace-cross Abstract: Plants in their natural habitats endure an array of interacting stresses, both biotic and abiotic, that rarely occur in isolation.
By Aswini Kumar Patra, Anshu Rastogi, Lingaraj Sahoo