Feature Selection

Drone RGB Image predicts above ground biomass of potatoes

The Yin et al. 2025 paper aims to understand which types of features extracted from drone RGB images predict potato aboveground biomass (AGB) more accurately across different growth stages. The innovation lies in the approach of integrating color indices with the gray-level co-occurrence matrix (GLCM) and Gabor wavelet textures. Our results demonstrated that combining the color (spectral) features and texture features could significantly enhance predictive accuracy, whereas different features varied a lot in their contributions to model performance.

Figure 4 from Yin et al. 2025
Also, multi-scale texture features lead to improved predictions when using the Gaussian Process Regression (GPR).

Last updated on Mar 31, 2025 1 min read Paper
Improving the transferability of potato nitrogen concentration estimation models based on hybrid feature selection and Gaussian process regression
Feature selection methods are widely used to improve the performance of plant nitrogen concentration (PNC) estimation models. However, …
DOI
Exploring the optimal wavelet function and wavelet feature for estimating maize leaf chlorophyll content
Ensuring global food security depends heavily on attaining and sustaining high maize yields. However, effective N fertilisation …
DOI
Mapping Plant Nitrogen Concentration and Aboveground Biomass of Potato Crops from Sentinel-2 Data Using Ensemble Learning Models
Excessive nitrogen (N) fertilization poses environmental risks at regional and global levels. Satellite remote sensing provides a novel …
DOI
Spectral Vegetation Indices to Track Senescence Dynamics in Diverse Wheat Germplasm
The ability of a genotype to stay green affects the primary target traits grain yield (GY) and grain protein concentration (GPC) in …
DOI