SIPEREA: A Scalable Imaging Platform for Measuring Two-Dimensional Growth of Duckweed
Source abstract
Abstract
Biomass production in organisms is closely linked to their growth rate, necessitating rapid, in situ, nondestructive, and accurate growth measurement. Existing imaging platforms are often limited by high cost, lack of scalability, wired connections, or insufficient automation, restricting their applicability for high-throughput growth monitoring. Here, we present SIPEREA, a scalable imaging platform built on cost-effective ESP32-CAM modules. SIPEREA comprises three graphical user interface (GUI) based applications: (1) an embedded program for the ESP32-CAM responsible for imaging, (2) an image acquisition program for automatic wireless image transmission from multiple ESP32-CAMs, and (3) an image analysis program that automatically segments organisms in the images using a deep neural network (DNN) and calculates their area. The implementation of asynchronous, sequential wireless image acquisition enables the efficient management of multiple ESP32-CAM modules. To demonstrate the usefulness of this platform, we analyzed images captured over a two-week period using four ESP32-CAM units during Lemna sp. (duckweed) cultivation to compute doubling time.
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Research context
OpenAlex topics: Constructed Wetlands for Wastewater Treatment · Innovations in Aquaponics and Hydroponics Systems · Peatlands and Wetlands Ecology
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Biological & Chemical Engineering, Hongik University, Sejong-ro 2639, Sejong 30016, Republic of Korea
Department of Chemical Engineering, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA
Funding
National Research Foundation of Korea
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