Evaluating two crop circle active canopy sensors for in-season diagnosis of winter wheat nitrogen status

Agronomy

Volumn 8, Issue 10, 2018, Pages

  Cao, Qiang ^a,b   Miao, Yuxin ^a,c   Shen, Jianning ^a   Yuan, Fei ^d   Cheng, Shanshan ^a   Cui, Zhenling ^a  

^a CHINA AGRICULTURAL UNIVERSITY   (China)

^b NANJING AGRICULTURAL UNIVERSITY   (China)

^c UNIVERSITY OF MINNESOTA   (United States)

^d MINNESOTA STATE UNIVERSITY   (United States)

Author keywords

Active canopy sensor; In season nitrogen management; Measurement height; Nitrogen nutrition index; Plant nitrogen concentration; Precision nitrogen management

Indexed keywords

EID: 85054053425     PISSN: None     EISSN: 20734395     Source Type: Journal    
DOI: 10.3390/agronomy8100201     Document Type: Article

References (42)

1. Miao, Y.; Stewart, B.A.; Zhang, F. Long-term experiments for sustainable nutrient management in China. A review. Agron. Sustain. Dev. 2011, 31, 397–414. [CrossRef]
2. Diacono, M.; Rubino, P.; Montemurro, F. Precision nitrogen management of wheat. A review. Agron. Sustain. Dev. 2013, 33, 219–241. [CrossRef]
3. Cola o, A.; Bramley, R. Do crop sensors promote improved nitrogen management in grain crops? Field Crops Res. 2018, 218, 126–140. [CrossRef]
4. Cao, Q.; Miao, Y.; Feng, G.; Gao, X.; Liu, B.; Liu, Y.; Li, F.; Khosla, R.; Mulla, D.J.; Zhang, F. Improving nitrogen use efficiency with minimal environmental risks using an active canopy sensor in a wheat-maize cropping system. Field Crops Res. 2017, 214, 365–372. [CrossRef]
5. Cao, Q.; Miao, Y.; Li, F.; Gao, X.; Liu, B.; Lu, D.; Chen, X. Developing a new crop circle active canopy sensor-based precision nitrogen management strategy for winter wheat in north China plain. Precis. Agric. 2017, 18, 2–18. [CrossRef]
6. Cao, Q.; Miao, Y.; Feng, G.; Gao, X.; Li, F.; Liu, B.; Yue, S.; Cheng, S.; Ustin, S.L.; Khosla, R. Active canopy sensing of winter wheat nitrogen status: An evaluation of two sensor systems. Comput. Electron. Agric. 2015, 112, 54–67. [CrossRef]
7. Bushong, J.T.; Mullock, J.L.; Miller, E.C.; Raun, W.R.; Klatt, A.R.; Arnall, D.B. Development of an in-season estimate of yield potential utilizing optical crop sensors and soil moisture data for winter wheat. Precis. Agric. 2016, 17, 451–469. [CrossRef]
8. Holland, K.H.; Lamb, D.W.; Schepers, J.S. Radiometry of proximal active optical sensors (aos) for agricultural sensing. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2012, 5, 1793–1802. [CrossRef]
9. Barker, D.W.; Sawyer, J.E. Factors affecting active canopy sensor performance and reflectance measurements. Soil Sci. Soc. Am. J. 2013, 77, 1673–1683. [CrossRef]
10. Kipp, S.; Mistele, B.; Schmidhalter, U. The performance of active spectral reflectance sensors as influenced by measuring distance, device temperature and light intensity. Comput. Electron. Agric. 2014, 100, 24–33. [CrossRef]
11. Solari, F. Developing a Crop Based Strategy for On-The-Go Nitrogen Management in Irrigated Cornfields. Ph.D. Thesis, University of Nebraska, Lincoln, NE, USA, August 2006.
12. Hatfield, J.L.; Prueger, J.H. Value of using different vegetative indices to quantify agricultural crop characteristics at different growth stages under varying management practices. Remote Sens. 2010, 2, 562–578. [CrossRef]
13. Li, F.; Miao, Y.; Hennig, S.D.; Gnyp, M.L.; Chen, X.; Jia, L.; Bareth, G. Evaluating hyperspectral vegetation indices for estimating nitrogen concentration of winter wheat at different growth stages. Precis. Agric. 2010, 11, 335–357. [CrossRef]
14. Yu, K.; Li, F.; Gnyp, M.L.; Miao, Y.; Bareth, G.; Chen, X. Remotely detecting canopy nitrogen concentration and uptake of paddy rice in the northeast China plain. ISPRS J. Photogram. Remote Sens. 2013, 78, 102–115. [CrossRef]
15. Eitel, J.U.H.; Keefe, R.F.; Long, D.S.; Davis, A.S.; Vierling, L.A. Active ground optical remote sensing for improved monitoring of seedling stress in nurseries. Sensors 2010, 10, 2843–2850. [CrossRef] [PubMed]
16. Erdle, K.; Mistele, B.; Schmidhalter, U. Comparison of active and passive spectral sensors in discriminating biomass parameters and nitrogen status in wheat cultivars. Field Crops Res. 2011, 124, 74–84. [CrossRef]
17. Shiratsuchi, L.; Ferguson, R.; Shanahan, J.; Adamchuk, V.; Rundquist, D.; Marx, D.; Slater, G. Water and nitrogen effects on active canopy sensor vegetation indices. Agron. J. 2011, 103, 1815–1826. [CrossRef]
18. Amaral, L.; Molin, J.; Portz, G.; Finazzi, F.; Cortinove, L. Comparison of crop canopy reflectance sensors used to identify sugarcane biomass and nitrogen status. Precis. Agric. 2015, 16, 15–28. [CrossRef]
19. Li, F.; Mistele, B.; Hu, Y.; Yue, X.; Yue, S.; Miao, Y.; Chen, X.; Cui, Z.; Meng, Q.; Schmidhalter, U. Remotely estimating aerial N status of phenologically differing winter wheat cultivars grown in contrasting climatic and geographic zones in China and Germany. Field Crops Res. 2012, 138, 21–32. [CrossRef]
20. Shaver, T.M.; Khosla, R.; Westfall, D.G. Evaluation of two ground-based active crop canopy sensors in maize: Growth stage, row spacing, and sensor movement speed. Soil Sci. Soc. Am. J. 2010, 74, 2101–2108. [CrossRef]
21. Cao, Q.; Miao, Y.; Shen, J.; Yu, W.; Yuan, F.; Cheng, S.; Huang, S.; Wang, H.; Yang, W.; Liu, F. Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with crop circle active crop canopy sensor. Precis. Agric. 2016, 17, 136–154. [CrossRef]
22. Holland Scientifc. Available online: https://hollandscientific.com/portfolio/crop-circle-acs-430/ (accessed on 11 September 2018).
23. Cui, Z.; Zhang, F.; Chen, X.; Miao, Y.; Li, J.; Shi, L.; Xu, J.; Ye, Y.; Liu, C.; Yang, Z.; et al. On-farm evaluation of an in-season nitrogen management strategy based on soil Nmin test. Field Crops Res. 2008, 105, 48–55. [CrossRef]
24. Lu, D.; Lu, F.; Pan, J.; Cui, Z.; Zou, C.; Chen, X.; He, M.; Wang, Z. The effects of cultivar and nitrogen management on wheat yield and nitrogen use efficiency in the north China plain. Field Crops Res. 2015, 171, 157–164. [CrossRef]
25. Large, E.C. Growth stages in cereals illustration of the feekes scale. Plant Pathol. 1954, 3, 128–129. [CrossRef]
26. Barnes, E.; Clarke, T.; Richards, S.; Colaizzi, P.; Haberland, J.; Kostrzewski, M.; Waller, P.; Choi, C.; Riley, E.; Thompson, T.; et al. Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. In Proceedings of the 5th International Conference on Precision Agriculture, Bloomington, MN, USA, 16–19 July 2000.
27. Gitelson, A.A.; Vi a, A.S.; Ciganda, V.N.; Rundquist, D.C. Remote estimation of canopy chlorophyll content in crops. Geophys. Res. Lett. 2005, 32, L08403. [CrossRef]
28. Horwitz, W. Official Methods of Analysis; Association of Official Agricultural Chemists: Washington, DC, USA, 1970; pp. 17–18.
29. Lemaire, G.; Jeuffroy, M.-H.; Gastal, F. Diagnosis tool for plant and crop n status in vegetative stage. Eur. J. Agron. 2008, 28, 614–624. [CrossRef]
30. Yue, S.; Meng, Q.; Zhao, R.; Li, F.; Chen, X.; Zhang, F.; Cui, Z. Critical nitrogen dilution curve for optimizing nitrogen management of winter wheat production in the north China plain. Agron. J. 2012, 104, 523–529. [CrossRef]
31. Lillesand, T.M.; Kiefer, R.W.; Chipman, J. Remote Sensing and Image Interpretation, 7th ed.; John Wiley & Sons: Hoboken, NJ, USA, 2015; pp. 575–581, ISBN 978-1-118-34328-9.
32. Winterhalter, L.; Mistele, B.; Schmidhalter, U. Evaluation of active and passive sensor systems in the field to phenotype maize hybrids with high-throughput. Field Crops Res. 2013, 154, 236–245. [CrossRef]
33. Miao, Y.; Mulla, D.J.; Randall, G.W.; Vetsch, J.A.; Vintila, R. Combining chlorophyll meter readings and high spatial resolution remote sensing images for in-season site-specific nitrogen management of corn. Precis. Agric. 2009, 10, 45–62. [CrossRef]
34. Ata-Ul-Karim, S.T.; Liu, X.; Lu, Z.; Zheng, H.; Cao, W.; Zhu, Y. Estimation of nitrogen fertilizer requirement for rice crop using critical nitrogen dilution curve. Field Crops Res. 2017, 201, 32–40. [CrossRef]
35. Cao, Q.; Miao, Y.; Wang, H.; Huang, S.; Cheng, S.; Khosla, R.; Jiang, R. Non-destructive estimation of rice plant nitrogen status with crop circle multispectral active canopy sensor. Field Crops Res. 2013, 154, 133–144. [CrossRef]
36. Mistele, B.; Schmidhalter, U. Estimating the nitrogen nutrition index using spectral canopy reflectance measurements. Eur. J. Agron. 2008, 29, 184–190. [CrossRef]
37. Prost, L.; Jeuffroy, M.-H. Replacing the nitrogen nutrition index by the chlorophyll meter to assess wheat N status. Agron. Sustain. Dev. 2007, 27, 321–330. [CrossRef]
38. Cao, Q.; Cui, Z.; Chen, X.; Khosla, R.; Dao, T.H.; Miao, Y. Quantifying spatial variability of indigenous nitrogen supply for precision nitrogen management in small scale farming. Precis. Agric. 2012, 13, 45–61. [CrossRef]
39. Xia, T.; Miao, Y.; Wu, D.; Shao, H.; Khosla, R.; Mi, G. Active optical sensing of spring maize for in-season diagnosis of nitrogen status based on nitrogen nutrition index. Remote Sens. 2016, 8, 605. [CrossRef]
40. Lu, J.; Miao, Y.; Shi, W.; Li, J.; Yuan, F. Evaluating different approaches to non-destructive nitrogen status diagnosis of rice using portable rapidscan active canopy sensor. Sci. Rep. 2017, 7, 14073. [CrossRef] [PubMed]
41. Chen, P. A comparison of two approaches for estimating the wheat nitrogen nutrition index using remote sensing. Remote Sens. 2015, 7, 4527–4548. [CrossRef]
42. Huang, S.; Miao, Y.; Zhao, G.; Yuan, F.; Ma, X.; Tan, C.; Yu, W.; Gnyp, M.; Lenz-Wiedemann, V.; Rascher, U.; et al. Satellite remote sensing-based in-season diagnosis of rice nitrogen status in northeast China. Remote Sens. 2015, 7, 10646–10667. [CrossRef]