A smartphone-based approach for comprehensive soil microbiome profiling

Abstract

The soil microbiome is crucial for nutrient cycling, health, and plant growth. This study presents a smartphone-based approach as a low-cost and portable alternative to traditional methods for classifying bacterial species and characterizing microbial communities in soil samples. By harnessing bacterial autofluorescence detection and machine learning algorithms, the platform achieved an average accuracy of 88% in distinguishing common soil-related bacterial species despite the lack of biomarkers, nucleic acid amplification, or gene sequencing. Furthermore, it successfully identified dominant species within various bacterial mixtures with an accuracy of 76% and three-level soil health identification at an accuracy of 80%–82%, providing insights into microbial community dynamics. The influence of other soil conditions (pH and moisture) was relatively minor, showcasing the platform's robustness. Various field soil samples were also tested with this platform at 80% accuracy compared with the laboratory analyses, demonstrating the practicality and usability of this approach for on-site soil analysis. This study highlights the potential of the smartphone-based system as a valuable tool for soil assessment, microbial monitoring, and environmental management.