Deep Learning-Based Automatic Duckweed Counting Using StarDist and Its Application on Measuring Growth Inhibition Potential of Rare Earth Elements as Contaminants of Emerging Concerns

In recent years, there have been efforts to utilize surface water as a power source, material, and food. However, these efforts are impeded due to the vast amounts of contaminants and emerging contaminants introduced by anthropogenic activities. Herbicides such as Glyphosate and Glufosinate are commonly known to contaminate surface water through agricultural industries. In contrast, some emerging contaminants, such as rare earth elements, have started to enter the surface water from the production and waste of electronic products. Duckweeds are angiosperms from the Lemnaceae family and have been used for toxicity tests in aquatic environments, mainly those from the genus Lemna, and have been approved by OECD. In this study, we used duckweed from the genus Wolffia, which is smaller and considered a good indicator of metal pollutants in the aquatic environment. The growth rate of duckweed is the most common endpoint in observing pollutant toxicity. In order to observe and mark the fronds automatically, we used StarDist, a machine learning-based tool. StarDist is available as a plugin in ImageJ, simplifying and assisting the counting process. Python also helps arrange, manage, and calculate the inhibition percentage after duckweeds are exposed to contaminants. The toxicity test results showed Dysprosium to be the most toxic, with an IC50 value of 14.6 ppm, and Samarium as the least toxic, with an IC50 value of 279.4 ppm. In summary, we can provide a workflow for automatic frond counting using StarDist integrated with ImageJ and Python to simplify the detection, counting, data management, and calculation process.

Automated summary

In recent years, efforts have been made to utilize surface water for various purposes, but the presence of contaminants introduced by human activities has hindered these efforts. Herbicides such as Glyphosate and Glufosinate are known to contaminate surface water through agriculture, while emerging contaminants like rare earth elements enter through electronic waste. Duckweeds, specifically from the Wolffia genus, are used as indicators of metal pollutants in aquatic environments. In this study, a machine learning-based tool called StarDist integrated with ImageJ and Python was used for automatic frond counting and toxicity analysis, with Dysprosium found to be the most toxic and Samarium the least toxic.