Seed classification with random forest models

作者：Josephine Elena Reek, Janneke Hille Ris Lambers, Eléonore Perret, Alana R. O. Chin · 发表于：Applications in Plant Sciences · 年份：2024 · DOI：10.1002/aps3.11596 · 被引用次数：6 · 研究领域：Cell Image Analysis Techniques、Smart Agriculture and AI、Remote Sensing and LiDAR Applications

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 potentially other objects. Automated seed classification is efficient and inexpensive, making it a practical solution that enhances the feasibility of large-scale monitoring projects in conservation biology.