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United States Department of Agriculture

Agricultural Research Service

Title: Predicting Corn and Soybean Grain Quality and Nutrient Content Using Hyperspectral Imagery, Topographic Attributes, and Soil Characteristics

Authors
item Fridgen, Jon - UNIV OF MO
item Wiebold, William - UNIV OF MO
item Kitchen, Newell
item Sudduth, Kenneth

Submitted to: Intnl Conference On Geospatial Information In Agriculture And Forestry
Publication Type: Other
Publication Acceptance Date: November 7, 2001
Publication Date: N/A

Technical Abstract: Determination of grain quality and, in some instances, nutrient content typically occurs after harvest. Prediction of grain nutrient content may be useful for determining the relative amount of nutrients removed from the field on a site-specific basis. Similarly, prediction of grain quality may be beneficial from a marketing standpoint. The objective of this paper was sto predict the quality and nutrient content of corn and soybean grain usin remotely sensed imagery, topographic attributes, and soil characteristics. Corn and soybean grain samples were taken at harvest and analyzed for nitrogen, phosphorous, and potassium. Further analysis was conducted with the soybean grain to determine protein, crude fat, and fatty acid content. Principal components regression, partial least squares regression, and stepwise multiple linear regression techniques were used to correlate grain attributes with the image, topographic, and soils data.

Last Modified: 12/22/2014
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