Geng, W.; Li, T.; Zhu, X.; Dou, L.; Liu, Z.; Qian, K.; Ye, G.; Lin, K.; Li, B.; Ma, X.;
et al. Predicting the Zinc Content in Rice from Farmland Using Machine Learning Models: Insights from Universal Geochemical Parameters. Appl. Sci. 2025, 15, 1273.
https://doi.org/10.3390/app15031273
AMA Style
Geng W, Li T, Zhu X, Dou L, Liu Z, Qian K, Ye G, Lin K, Li B, Ma X,
et al. Predicting the Zinc Content in Rice from Farmland Using Machine Learning Models: Insights from Universal Geochemical Parameters. Applied Sciences. 2025; 15(3):1273.
https://doi.org/10.3390/app15031273
Chicago/Turabian Style
Geng, Wenda, Tingting Li, Xin Zhu, Lei Dou, Zijia Liu, Kun Qian, Guiqi Ye, Kun Lin, Bo Li, Xudong Ma,
and et al. 2025. "Predicting the Zinc Content in Rice from Farmland Using Machine Learning Models: Insights from Universal Geochemical Parameters" Applied Sciences 15, no. 3: 1273.
https://doi.org/10.3390/app15031273
APA Style
Geng, W., Li, T., Zhu, X., Dou, L., Liu, Z., Qian, K., Ye, G., Lin, K., Li, B., Ma, X., Hou, Q., Yu, T., & Yang, Z.
(2025). Predicting the Zinc Content in Rice from Farmland Using Machine Learning Models: Insights from Universal Geochemical Parameters. Applied Sciences, 15(3), 1273.
https://doi.org/10.3390/app15031273
