arXiv Machine Learning 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

Spectroscopy Analysis with Machine Learning Regression for the Quantification of Carbon and Nitrogen Contents in Inceptisol and Oxisol Soil Types: Comparing Different Preprocessing and Validation methods as well as Feature Importance

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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.

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arXiv Machine Learning
Aug 11

Integrating spectral and morphological plant features with decision-tree models for early-season cotton biomass and nitrogen status estimation from multi-year UAV data

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
arXiv Machine Learning
Aug 14

A Multispectral Framework for the Detection of Calcium Carbide-Induced Ripening and Shelf-Life Estimation in Climacteric Fruits

arXiv:2608. 13073v1 Announce Type: new Abstract: Significant health risks are associated with the illegal, yet commonly practiced use of industrial-grade Calcium Carbide (CaC2) for ripening climacteric fruits like mango and banana, which leaves behind trace residues of arsenic and phosphorus.

By Gurbhit Chaurakoti, Harshit Kumar, Hani Kumar, Anurag Singh, Ram Asrey
arXiv Machine Learning
Jun 5

Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models

arXiv:2605. 13587v3 Announce Type: replace-cross Abstract: Preprocessing screening is often the most expensive part of a near-infrared spectroscopy calibration workflow.

By Gregory Beurier (CIRAD, UMR AGAP Institut, Montpellier, France, UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France), Robin Reiter (CIRAD, UMR AGAP Institut, Montpellier, France, UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France), Camille No\^us (Laboratoire Cogitamus), Lauriane Rouan (CIRAD, UMR AGAP Institut, Montpellier, France, UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France), Denis Cornet (CIRAD, UMR AGAP Institut, Montpellier, France, UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France)
arXiv AI
Aug 24

Decision Tree and K-Means Analysis of Raman Spectra for Edible Oils: A Physics-Informed AI Approach

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