How extract the features of the complex systems of rhizobacterial microbiomes, and efficiently control the microbiomes?

For self-organizing complex systems, feature extraction methods such as principal component analysis and clustering analysis are often used. Conversely, it means that there is no other good analysis methodology close at hand. In self-organizing complex systems, the properties of the whole can often be neither understood nor predicted by knowledge of its constituents alone, and the interactions between the constituents of the system give rise to new structures and functions, and those will appear on a large scale.
In the case of the rhizobacterial microbiomes, it is possible that when good bacteria is exceeded a certain threshold, opportunistic bacteria become allies and a synergistic effect appears remarkably. The reverse is also true, and when the number of bad bacteria exceeds a certain threshold, the plant dies at once.

The problem is how to perform feature extraction (in other words, pattern recognition) of such a complex system of rhizobacterial microbiome in the field (farm). At the research level, 16S rRNA read analysis of the rhizobacterial microbiomes and computer analysis on the full use of the obtained big data might be able to visualize the hidden patterns which could not be identified with  conventional methods. But, can such a method be used in the field? No, this kind of work will remain in the world of papers. If the technology is not easy for anyone to use, it will not succeed in reality as a business. To be used on site, it has to be cheap, and it has to give immediate results.

Bacillus, Pseudomonas, Streptomyces, Arthrobacter are said to be representative good bacteria as rhizobacteria, and Enterobacter and Barkholderia are also to be good bacteria.
The bacterial classification of such representative good bacteria and bad bacteria is written below.
First of all, good bacteria,
Actinobacteria phylum→ Actinobacteria class → Streptomyces order → Streptomycetae → Streptomyces genus (good guys)
Proteobacteria phylum → γ-Proteobacterial class → Pseudomonad order → Pseudomonadidae → Pseudomonas genus (typical good guys)
Proteobacteria phylum → γ-Proteobacterial class → Enterobacter order → Enterobacteraceae → Enterobacter genus (good guy)
Proteobacteria phylum → β-Proteobacterial class → Barkholderia order → Barkholderidae → Balkholderia genus (good)
Firmicutes phylum→ Bacillus class → Bacillus order → Bacillus family → Bacillus genus (typical good guy)
Actinobacteria phylum→ Actinobacteria class → Actinobacteria family→ Micrococcaceae → Arthrobacter genus (good guys)
and bad bacteria,
Ascomycota phylum → Hydrangea class → Pistanthus → Asteraceae → Fusarium genus (typical bad guys)

If we can sense the phylum Actinobacteria, Proteobacteria, Firmicutes, and Actinobacteria, it will be possible that we can grasp major patterns in the rhizosphere, I think.
For your information, Proteobacteria are Gram-negative bacteria, while Actinobacteria, Firmicutes, and Actinobacteria are Gram-positive bacteria.