Access the full text.
Sign up today, get DeepDyve free for 14 days.
M. L. Stone, J. B. Solie, R. W. Whitney, W. R. Raun, H. L. Lees (1996)
Sensors for detection of nitrogen in winter wheat SAE Technical paper series. SAE Paper No. 961757
P. Feiffer, A. Feiffer, R. Schwaiberger, P. Leithold, J. Jasper, A. Link (2003)
HYDRO N-SENSOR
M. Stone, J. Solie, R. Whitney, W. Raun, H. Lees (1996)
Sensors for Detection of Nitrogen in Winter Wheat
(1999)
Satellite imagery for crop stress and yield prediction: cotton in Mississippi
Driss Haboudanea, John Millera, Nicolas Tremblayc, P. Zarco-Tejadad, Louise Dextrazec (2002)
Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agricultureRemote Sensing of Environment, 81
(1999)
Nitrogen status detection of corn leaves by reflectance technique
(1997)
How a Corn Plant Develops, Special Report No
W. Lee, S. W. Searcy, T. Kataoka (1999)
Assessing nitrogen stress in corn varieties of varying color. ASAE Paper No. 993034
W. Piekielek, R. Fox, J. Toth, K. Macneal (1995)
Use of a Chlorophyll Meter at the Early Dent Stage of Corn to Evaluate Nitrogen SufficiencyAgronomy Journal, 87
S. Tumbo, D. Wagner, P. Heinemann (2002)
HYPERSPECTRAL–BASED NEURAL NETWORK FOR PREDICTING CHLOROPHYLL STATUS IN CORNTransactions of the ASABE, 45
(1997)
The solar activity cycle and sun-as-a-star variability in the visible and infrared
J. Read, L. Tarpley, J. McKinion, K. Reddy (2002)
Narrow-waveband reflectance ratios for remote estimation of nitrogen status in cotton.Journal of environmental quality, 31 5
D. Smeal, Hailin Zhang (1994)
Chlorophyll meter evaluation for nitrogen management in cornCommunications in Soil Science and Plant Analysis, 25
(1998)
Integration of neural networks with a spectral reflectance sensor to detect nitrogen deficiency in cotton
(1998)
Hyperspectral characteristics of nitrogen deficient corn
W. Lee, S. Searcy, T. Kataoka (1999)
ASSESSING NITROGEN STRESS IN CORN VARIETIES OF VARYING COLOR
S. Tumbo, D. Wagner, P. Heinemann (2001)
On-the-go Sensing of Chlorophyll Status in Corn, 45
S. Tumbo, D. Wagner, P. Heinemann (2000)
HYPERSPECTRAL CHARACTERISTICS OF CORN PLANTS UNDER DIFFERENT CHLOROPHYLL LEVELSTransactions of the ASABE, 45
S. Tumbo, D. Wagner, P. Heinemann (2000)
Hyperspectral input-based neural network model for predicting chlorophyll status in corn.
J. Hanway (1966)
How a corn plant develops
W. C. Bausch, K. Diker, A. F. H. Goetz, B. Curtis (1998)
Hyperspectral characteristics of nitrogen deficient corn. ASAE Paper No. 983061
Commercial agriculture has come under increasing pressure to reduce nitrogen fertilizer inputs in order to minimize potential non-point source pollution of ground and surface waters. This has resulted in increased interest in site-specific fertilizer management. This research aimed to develop techniques for real time assessment of nitrogen status of corn using a mobile sensor with the potential to regulate nitrogen application based on data from that sensor. Specifically, the research attempted to determine the system parameters necessary to optimize reflectance spectra of corn plants as a function of growth stage and nitrogen status. An adaptable, multi-spectral sensor and the signal processing algorithm to provide real time, in-field assessment of corn nitrogen status were developed.
Precision Agriculture – Springer Journals
Published: Jan 5, 2005
Access the full text.
Sign up today, get DeepDyve free for 14 days.