Machine learning on multi-spectral imagery to estimate nutrient yield of mixed-species cover crops

Abstract

This study aimed to estimate mixed-species cover crop (CC) biomass and nutrient contents using remote sensing, as ground-based measurements are time-consuming and costly. Eleven CC treatments with varying grass-legume proportions (GLP) were sampled, and nutrient contents were determined along with multispectral imagery captured during the first and fourth weeks of March and the fourth week of April 2023. Biomass N (R² = 0.46–0.60) and K% (R² = 0.41—0.71) decreased with increasing GLP. The chlorophyll absorption ratio index and the normalized difference vegetation index closely followed the biomass nutrients N, P, and K combined yield (Bio_NPK) trend. Machine learning algorithms random forest (RF) and partial least square (PLS) regression were better for biomass (R² = 0.74 with RF) and N% (R² = 0.72 with PLS) prediction compared to the Bio_NPK prediction. These results are crucial for scientists to devise appropriate analysis approaches for estimating the benefits of mixed-species CC.