Article Versions Notes

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

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

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