Using preprocessed datasets to construct and interpret multiclass identification models.

ABSTRACT:

Introduction

Image and near-infrared (NIR) spectroscopic data are widely used for constructing analytical models in precision agriculture. While model interpretation can provide valuable insights for quality control and improvement, the inherent ambiguity of individual image pixels or spectral data points often hinders practical interpretability when using raw data directly. Furthermore, the presence of imbalanced datasets can lead to model overfitting and consequently, poor robustness. Therefore, developing alternative approaches for constructing interpretable and robust models using these data types is crucial.

Methods

This study proposes using preprocessed data-specifically, morphological features extracted from images and chemical component concentrations predicted from

SUBMITTER: Wang C 

PROVIDER: S-EPMC12405167 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature