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

Pablo José Menjívar,

Andrés Felipe Solís Pino, Julio Eduardo Mejía Manzano

и другие.

Microorganisms, Год журнала: 2025, Номер 13(4), С. 860 - 860

Опубликована: Апрель 9, 2025

Phosphorus is an important macronutrient for plant development, but its bioavailability in soil often limited. Phosphate-solubilizing microorganisms play a vital role phosphorus biogeochemistry, offering sustainable alternative to chemical fertilizers, which pose environmental risks. Manual measurements quantifying phosphate solubilization capacity are laborious, subjective, and time-consuming, so there need develop more efficient objective approaches. This study aimed validate machine vision system called IGLOO automate optimize the determination of relative efficiency phosphate-solubilizing bacteria. was developed using YOLOv8 conjunction with creating labeling dataset images bacterial colonies grown vitro strains Enterobacter R11 FCRK4. The model trained different number epochs. IGLOO’s performance evaluated by comparing segmentation accuracy accepted metrics domain contrasting estimates experts’ manual measurements. achieved greater than 90% colony halo detection, error less 6% compared measurements, demonstrating reliability minimizing observer variability. Finally, represents significant advance quantitative evaluation because it reduces analysis time provides reproducible results agricultural studies.

Язык: Английский

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

Pablo José Menjívar,

Andrés Felipe Solís Pino, Julio Eduardo Mejía Manzano

и другие.

Microorganisms, Год журнала: 2025, Номер 13(4), С. 860 - 860

Опубликована: Апрель 9, 2025

Phosphorus is an important macronutrient for plant development, but its bioavailability in soil often limited. Phosphate-solubilizing microorganisms play a vital role phosphorus biogeochemistry, offering sustainable alternative to chemical fertilizers, which pose environmental risks. Manual measurements quantifying phosphate solubilization capacity are laborious, subjective, and time-consuming, so there need develop more efficient objective approaches. This study aimed validate machine vision system called IGLOO automate optimize the determination of relative efficiency phosphate-solubilizing bacteria. was developed using YOLOv8 conjunction with creating labeling dataset images bacterial colonies grown vitro strains Enterobacter R11 FCRK4. The model trained different number epochs. IGLOO’s performance evaluated by comparing segmentation accuracy accepted metrics domain contrasting estimates experts’ manual measurements. achieved greater than 90% colony halo detection, error less 6% compared measurements, demonstrating reliability minimizing observer variability. Finally, represents significant advance quantitative evaluation because it reduces analysis time provides reproducible results agricultural studies.

Язык: Английский

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