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Rhea, S.T
Lu, J
Mochizuki, R
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Authors
Mochizuki, R
Han-ya, I
Noguchi, N
Su, B
Ishii, K
Lu, J
Miao, Y
Huang, Y
Shi, W
Ferreyra, R
Applegate, D.B
Berger, A.W
Berne, D.T
Craker, B.E
Daggett, D.G
Gowler, A
Bullock, R.J
Haringx, S.C
Hillyer, C
Howatt, T
Nef, B.K
Rhea, S.T
Russo, J.M
Nieman, S.T
Sanders, P
Wilson, J.A
Wilson, J.W
Tevis, J.W
Stelford, M.W
Shearouse, T.W
Schultz, E.D
Reddy, L
Lu, J
Wang, H
Miao, Y
Lu, J
Chen, Z
Miao, Y
Li, Y
Zhang, Y
Zhao, X
Jia, M
Lacerda, L
Miao, Y
Sharma, V
E. Flores, A
Kechchour, A
Lu, J
Miao, Y
Kechchour, A
Sharma, V
Flores, A
Lacerda, L
Mizuta, K
Lu, J
Huang, Y
Mizuta, K
Miao, Y
Lu, J
Negrini, R.P
Lu, J
Miao, Y
Ransom, C.J
Fernández, F
Topics
Remote Sensing Applications in Precision Agriculture
Unmanned Aerial Systems
Standards & Data Stewardship
Precision Agriculture and Global Food Security
In-Season Nitrogen Management
Proximal and Remote Sensing of Soils and Crops (including Phenotyping)
On Farm Experimentation with Site-Specific Technologies
In-Season Nitrogen Management
Type
Poster
Oral
Year
2012
2016
2018
2022
2024
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Authors

Filter results1 paper(s) found.

1. In-season Diagnosis of Winter Wheat Nitrogen Status Based on Rapidscan Sensor Using Machine Learning Coupled with Weather Data

Nitrogen nutrient index (NNI) is widely used as a good indicator to evaluate the N status of crops in precision farming. However, interannual variation in weather may affect vegetation indices from sensors used to estimate NNI and reduce the accuracy of N diagnostic models. Machine learning has been applied to precision N management with unique advantages in various variables analysis and processing. The objective of this study is to improve the N status diagnostic model for winter wheat by combining... J. Lu, Z. Chen, Y. Miao, Y. Li, Y. Zhang, X. Zhao, M. Jia