Ranking-guided coreset selection for NIRS-based nutrient prediction in liquid organic manures
Year: 2025
Type: article
Abstract: Accurate nutrient estimation in liquid organic manures (LOM) is crucial to avoid over-fertilization and to support precision agriculture. While near-infrared spectroscopy (NIRS) provides a fast and non-destructive method for nutrient prediction, its practical adoption remains limited by the lack of efficient calibration strategies that balance model accuracy with resource constraints. This study proposes a relevance-guided coreset selection approach to improve model performance by selectively expanding the calibration set with the most informative samples. A dataset comprising 507 cattle and 439 pig manure samples was used to predict four key LOM properties: dry matter (DM), ammonium nitrogen (NH4-N), total nitrogen (TN), and phosphorus (P2O5). Relevance scores were computed via repeated model training using a Shapley-like attribution method. As a representative example for DM in cattle, training on the full calibration data (405 samples) yielded R2 = 0.81 on the validation data (102 samples). Using only half the calibration data (211 samples) plus the top 10 % most relevant samples (19 coreset samples) increased performance to R2 = 0.83, whereas adding the bottom 10 % (19 anti-coreset samples) reduced it to R2 = 0.78. A feature-based learning-to-rank model, trained to generalize relevance estimation to unseen data, selected comparably informative samples and achieved R2 = 0.82 under the same reduced-data configuration. This result shows that the ranking model effectively identifies informative samples and enables targeted calibration expansion with minimal laboratory effort, thus making NIRS scalable and resource-efficient in smart agriculture.
Source: Smart Agricultural Technology
Authors: Leonard Friedrich, Karl-Ludwig Krieger
Institution: University of Bremen
Language: English
Subfield: Analytical Chemistry
Field: Chemistry
Domain: Physical Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $1,770
OpenAlex ID: W4415628899