Seed classification with random forest models.

ABSTRACT:

Premise

To improve forest conservation monitoring, we developed a protocol to automatically count and identify the seeds of plant species with minimal resource requirements, making the process more efficient and less dependent on human operators.

Methods and results

Seeds from six North American conifer tree species were separated from leaf litter and imaged on a flatbed scanner. In the most successful species-classification approach, an ImageJ macro automatically extracted measurements for random forest classification in the software R. The method allows for good classification accuracy, and the same process can be used to train the model on other species.

Conclusions

This protocol is an adaptable tool for efficient and consistent identification of seed species or po

SUBMITTER: Reek JE 

PROVIDER: S-EPMC11192156 | biostudies-literature | 2024 May-Jun

REPOSITORIES: biostudies-literature