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.
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.
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.
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.
arXiv:2605. 13587v3 Announce Type: replace-cross Abstract: Preprocessing screening is often the most expensive part of a near-infrared spectroscopy calibration workflow.
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.
arXiv:2606. 05731v1 Announce Type: new Abstract: In-season crop type mapping is critical for food security in the face of increasingly extreme climate-related threats to crops.
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.
arXiv:2609.39756v1 Announce Type: new Abstract: This article introduces an algorithm that uses a U-Net architecture to determine vertical ground surface displacements from unmanned aerial vehicle (UA...
The study evaluates how feature engineering (FE) affects machine learning models for ocean colour data, proposing a seven‑step optimisation framework that includes band selection, scaling, normalisation, index extraction, PCA, and feature scaling. Applied to Sentinel‑3 OLCI observations, the framework improves model accuracy for estimating Chlorophyll‑a and Secchi disk depth, achieving higher R values and lower mean absolute errors compared to standard algorithms. However, the optimal FE varies across targets and models, indicating that FE optimisation must be tailored to each application.
The paper introduces KAINN, a hybrid neural‑mechanistic model that augments the Agriculture‑informed Neural Network with domain knowledge on fertilizer diffusion, soil respiration, and water‑filled porosity to predict nitrous oxide emissions from agriculture. Experiments across CNN, LSTM, and Transformer architectures show that KAINN achieves lower root mean square error, lower mean absolute error, and higher R-squared values compared to purely data‑driven models and the original AINN. The learned interfaces exhibit smoother, more physically consistent parameter trajectories with reduced uncertainty.
arXiv:2608. 19899v1 Announce Type: cross Abstract: Machine learning (ML) is widely used for the development of ocean colour algorithms, but most studies focus on model parameter training and hyperparameter tuning.
arXiv:2608. 11638v1 Announce Type: new Abstract: Spatially continuous quantification of forest above-ground biomass (AGB) is what makes carbon accounting credible and mitigation strategies actionable.