Cite
Minimizing active canopy sensor differences in nitrogen status diagnosis and in-season nitrogen recommendation for maize with multi-source data fusion and machine learning.
MLA
Wang, Xinbing, et al. “Minimizing Active Canopy Sensor Differences in Nitrogen Status Diagnosis and In-Season Nitrogen Recommendation for Maize with Multi-Source Data Fusion and Machine Learning.” Precision Agriculture, vol. 24, no. 6, Dec. 2023, pp. 2549–65. EBSCOhost, https://doi.org/10.1007/s11119-023-10052-6.
APA
Wang, X., Miao, Y., Dong, R., & Kusnierek, K. (2023). Minimizing active canopy sensor differences in nitrogen status diagnosis and in-season nitrogen recommendation for maize with multi-source data fusion and machine learning. Precision Agriculture, 24(6), 2549–2565. https://doi.org/10.1007/s11119-023-10052-6
Chicago
Wang, Xinbing, Yuxin Miao, Rui Dong, and Krzysztof Kusnierek. 2023. “Minimizing Active Canopy Sensor Differences in Nitrogen Status Diagnosis and In-Season Nitrogen Recommendation for Maize with Multi-Source Data Fusion and Machine Learning.” Precision Agriculture 24 (6): 2549–65. doi:10.1007/s11119-023-10052-6.