Predicting within-field variability in grain yield and protein content of winter wheat using UAV-based multispectral imagery and machine learning approaches

Author:

Zhou Xinbin1,Kono Yusuke2,Win Aung3,Matsui Tsutomu2,Tanaka Takashi S. T.24ORCID

Affiliation:

1. College of Resources and Environment, Southwest University, Chongqing, China

2. Faculty of Applied Biological Sciences, Gifu University, Gifu, Japan

3. The United Graduate School of Agricultural Science, Gifu University, Gifu, Japan

4. Artificial Intelligence Advanced Research Center, Gifu University, Gifu, Japan

Funder

JSPS

Publisher

Informa UK Limited

Subject

Agronomy and Crop Science

Reference46 articles.

1. Abadi, M., Agarwal., A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefow- icz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Watten- berg, M., Wicke, M., Yu, Y. & Zheng, X. (2015). TensorFlow: Large-scale machine learning on heterogeneous systems. Software Retrieved October 31, 2019 from tensorflow.org

2. Variation of grain nitrogen content in relation with grain yield in old and modern Spanish wheats grown under a wide range of agronomic conditions in a Mediterranean region

3. ArduPilot Dev Team. (2019). Mission planner home. Retrieved October 27, 2019 from http://ardupilot.org/planner/index.html

4. Simulation of grain protein content with APSIM-Nwheat

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