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

• Published in: Toxics (Basel) Vol. 11; no. 8; p. 680
• Main Authors: Kurnia, Kevin Adi, Lin, Ying-Ting, Farhan, Ali, Malhotra, Nemi, Luong, Cao Thang, Hung, Chih-Hsin, Roldan, Marri Jmelou M., Tsao, Che-Chia, Cheng, Tai-Sheng, Hsiao, Chung-Der
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2023; MDPI
• Subjects: Agricultural industry; Angiosperms; Anthropogenic factors; Aquatic environment; Aquatic plants; cell counting; Contaminants; Contamination; Data management; Deep learning; Duckweed; Dysprosium; Environmental aspects; Environmental indicators; Floating plants; Food sources; Fronds; Glufosinate; Glyphosate; Growth; Herbicides; image segmentation; Lemnaceae; Machine learning; Mathematical analysis; Methods; Physiological aspects; Pollutants; Power sources; rare earth element; Rare earth elements; Rare earth metals; Samarium; Software; StarDist; Surface water; Testing; Toxicity; Toxicity testing; Trace elements; Water pollution; Workflow; Taiwan; Japan

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 comm...