American Psychological Association 6th edition

Shi, Y., Xu, R., & Qi, Z. (2025). MSNet: A Novel Deep Learning Framework for Efficient Missing Seedling Detection in Maize Fields. Applied Artificial Intelligence, 39(1), 1-25. https://doi.org/10.1080/08839514.2025.2469372

ISO-690 (author-date, English)

SHI, Yong, XU, Ruijie und QI, Zhiquan, 2025. MSNet: A Novel Deep Learning Framework for Efficient Missing Seedling Detection in Maize Fields. Applied Artificial Intelligence. 1 Dezember 2025. Vol. 39, no. 1, p. 1-25. DOI 10.1080/08839514.2025.2469372.

Modern Language Association 9th edition

Shi, Y., R. Xu, und Z. Qi. „MSNet: A Novel Deep Learning Framework for Efficient Missing Seedling Detection in Maize Fields.“. Applied Artificial Intelligence, Bd. 39, Nr. 1, Dezember 2025, S. 1-25, https://doi.org/10.1080/08839514.2025.2469372.

Mohr Siebeck - Recht (Deutsch - Österreich)

Shi, Yong/Xu, Ruijie/Qi, Zhiquan: MSNet: A Novel Deep Learning Framework for Efficient Missing Seedling Detection in Maize Fields., Applied Artificial Intelligence 2025, 1-25.

Emerald - Harvard

Shi, Y., Xu, R. und Qi, Z. (2025), „MSNet: A Novel Deep Learning Framework for Efficient Missing Seedling Detection in Maize Fields.“, Applied Artificial Intelligence, Vol. 39 No. 1, S. 1-25.

Achtung: Diese Zitate sind unter Umständen nicht zu 100% korrekt.