IGLOO: Machine Vision System for Determination of Solubilization Index in Phosphate-Solubilizing Bacteria

Area of Science:

• Agricultural Microbiology
• Biogeochemistry
• Machine Vision

Background:

• Phosphorus is crucial for plant growth, but soil bioavailability is often limited.
• Phosphate-solubilizing microorganisms enhance phosphorus availability, presenting a sustainable alternative to chemical fertilizers.
• Current manual methods for quantifying phosphate solubilization are inefficient and subjective.

Purpose of the Study:

• To develop and validate IGLOO, a machine vision system for automated phosphate solubilization efficiency determination.
• To optimize the quantitative evaluation of phosphate-solubilizing bacteria.

Main Methods:

• Developed IGLOO using YOLOv8 object detection model.
• Created and labeled a dataset of bacterial colonies (Enterobacter R11, FCRK4) grown in vitro.
• Trained the YOLOv8 model with varying epochs and evaluated performance using segmentation accuracy and comparison with manual measurements.

Main Results:

• IGLOO achieved over 90% accuracy in detecting bacterial colonies and solubilization halos.
• The system demonstrated a relative error of less than 6% compared to expert manual measurements.
• IGLOO significantly reduced analysis time and observer variability.

Conclusions:

• IGLOO provides an objective, reproducible, and efficient method for evaluating phosphate solubilization efficiency.
• This advancement supports sustainable agricultural practices by optimizing the use of phosphate-solubilizing microorganisms.
• The machine vision system minimizes subjectivity and enhances the reliability of quantitative microbial analysis.