Image analysis-based quantification of fungal sporulation by automatic conidia counting and gray value correlation

Area of Science:

• Mycology
• Agricultural Science
• Biotechnology

Background:

• Accurate quantification of fungal spore discharge is crucial for biological pest control.
• Existing methods for conidia quantification can be time-consuming and subjective.
• There is a need for rapid, reproducible, and accessible methods for fungal sporulation analysis.

Purpose of the Study:

• To develop and present a novel computer-assisted image analysis method for quantifying actively discharged fungal spores.
• To provide a user-friendly manual for laboratories, enabling objective and reproducible spore quantification.
• To facilitate large-scale sample analysis and quality control of fungal formulations.

Main Methods:

• Utilized bright field microscopy, standard scanners, and the open-source software ImageJ for image acquisition and analysis.
• Developed a computer-assisted image analysis protocol for rapid and objective fungal spore counting.
• Established a linear relationship between image gray value and conidia count for simplified quantification.

Main Results:

• The developed method allows for rapid, simple, objective, and reproducible quantification of fungal spores.
• The technique is suitable for large-scale sample quantities and requires only conventional laboratory equipment.
• Image gray value serves as a single, reliable parameter for quantification after initial calibration.

Conclusions:

• The presented image analysis method offers an efficient solution for fungal sporulation capacity determination.
• This approach simplifies quality control for fungal formulations used in biological pest control.
• The method's accessibility and scalability make it valuable for research and industrial applications.