Exploring crop health and its associations with fungal soil microbiome composition using machine learning applied to remote sensing data

Global food security is increasingly challenged by climate change and unsustainable agriculture, emphasizing the need for strategies to enhance crop productivity. Understanding the interplay between crop health and soil microbiomes is crucial. This study explores the link between crop health, observed via multi-spectral satellite imagery, and fungal soil microbiome taxonomy. We associate the normalized difference vegetation index with fungal microbiomes in wheat, barley, and maize using a two-step machine learning process. The first step adjusts normalized difference vegetation index values for abiotic confounders using a random forest model trained on Lucas 2018 topsoil and ERA5 climate datasets. The second step clusters operational taxonomy unit counts from fungal DNA, revealing significant differences in residual normalized difference vegetation index values. To identify potential bio-fertilizer candidates, we compare the average relative abundance of operational taxonomy unit clusters and construct sparse biological networks. Key findings are: (I) clusters with higher plant pathogenic genera have lower normalized difference vegetation index values; (II) clusters with higher influential scores for multiple beneficial genera have higher normalized difference vegetation index values; (III) lower abundance taxonomy (1-3%) seems to regulate microbial networks; (IV) the influence of beneficial vs. pathogenic taxonomy is relative to their abundance. The study links satellite imagery to fungal microbiomes, providing a baseline for exploring fungal bio-fertilizers.