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: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:2607. 00834v1 Announce Type: new Abstract: Near-Infrared (NIR) spectroscopy has emerged as a promising alternative to traditional soil analysis methods, offering advantages such as speed, low cost, and non-destructive testing.
By Vinicius Herique Kieling, Guilherme Macedo Baggio, Felipe Augusto Bueno Rossi, Marco Antonio de Castro Barbosa, Dalcimar Casanova, Larissa Macedo dos Santos Tonial, Jefferson Tales Oliva
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.
The paper presents a Raman spectroscopy and machine‑learning framework that uses t‑SNE, K‑means, Decision Trees, and NNLS‑based spectral decomposition to authenticate edible oils. In pure oils, Decision Trees achieved 100% accuracy using only four Raman variables out of 1866 features, while in matrix‑containing samples, NNLS‑based PI‑AI improved classification to about 85–86% with only four to five key variables. The approach yields highly compact, interpretable spectral representations that enable accurate oil identification with minimal data footprint.
By Amrita Shaw, Chandrasekar S. N., Sai Muthukumar V., Jhinuk Gupta, Deepak L. N. Kallepalli
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