Forabot: Automated Planktic Foraminifera Isolation and Imaging. (8th December 2022)

• Record Type: Journal Article
• Authors: Richmond, Turner; Cole, Jeremy; Dangler, Gabriella; Daniele, Michael; Marchitto, Thomas; Lobaton, Edgar
• Is Part Of: Geochemistry, geophysics, geosystems. Volume 23:Number 12(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-08
• Subjects: foraminifera -- automation -- classification; Geochemistry -- Periodicals; Geophysics -- Periodicals; Earth sciences -- Periodicals; 550.5
• DOI: 10.1029/2022GC010689
• Languages: English
• ISSNs: 1525-2027

Abstract

Abstract: Physical inspection and sorting of foraminifera is a necessity in many research labs, as foraminifera serve as paleoenvironmental and chronostratigraphic indicators. In order to gain counts of species from samples, analyze chemical compositions, or extract morphological properties of foraminifera, research labs require human time and effort handling and sorting these microscopic fossils. The presented work describes Forabot, an open‐source system which can physically manipulate individual foraminifera for imaging and isolation with minimal human interaction. The major components to build a Forabot are outlined in this work, with supplementary information available which allows for other researchers to build a Forabot with low‐cost, off‐the‐shelf components. From a washed and sieved sample of hundreds of foraminifera, the Forabot is shown to be capable of isolating and imaging individual forams. The timing of the Forabot's current pipeline allows for the processing of up to 27 foram specimens per hour, a rate that can be improved for future classification purposes by reducing image quality and/or quantity. Along with the physical descriptions, the image processing and classification pipelines are also reviewed. A proof‐of‐concept classifier utilizes a finetuned VGG‐16 network to achieve a classification accuracy of 79% on a validation set of foraminifera images collected with Forabot. In conclusion, the system is able to be built by researchers for a low cost, effectively manipulate foraminifera with few mistakes, provide quality images for future research, and classify the species of imaged forams. Plain Language Summary: Foraminifera or "forams" are abundant microscopic organisms found in the ocean, and their shells are a common component of seafloor mud. Mud cores can be used to understand ancient ocean conditions, and the types and chemistry of forams in a sample are useful environmental indicators. However, separating different types of forams is slow and tedious work which requires considerable expertise. We have designed and built a robot called Forabot, which picks up individual shells, takes high‐quality photographs of them, and moves them to a bin for sorting. We describe the system so that other researchers can build their own Forabot at low cost. The current version of Forabot is optimized for high‐quality imaging and is therefore relatively slow, but if it is instead used for classifying and sorting shells into different types, it can be optimized for speed. We discuss the preliminary performance of a classifier based on artificial intelligence, with overall accuracy of 79%. In conclusion, our robot can be built by researchers for a low cost, effectively manipulate forams with few mistakes, provide quality images for future research, and accurately classify the type of foram. Key Points: The Forabot is a low‐cost and open‐source system for automated isolation and imaging of foraminifera We explore Forabot performance as a physical manipulation tool Forabot images are classified using deep learning with promising results for future integration