Click on “Download PDF” for the PDF version or on the title for the HTML version.

If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.


Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Paper number  013116,  2001 ASAE Annual Meeting. (doi: 10.13031/2013.7428) @2001
Authors:   Chun-Chieh Yang, Shiv O. Prasher, Joann Whalen, Pradeep K. Goel
Keywords:   data mining, decision tree, hyperspectral, fertilizer, manure, remote sensing

This paper introduces data mining technology designed to classify agricultural fields under different manure/fertilizer application strategies. During the summer of 2000, airborne hyperspectral data was collected three times at two field sites in southwestern Quebec, Canada. One field site contained 24 plots (20 m x 24 m) that were amended with manure treatments and planted with corn and soybeans. The second field site contained 18 plots (18.5 m x 75 m) that received chemical fertilizers and were planted with corn. Reflectances of 72 wave bands of hyperspectral data (400 nm for violet to 950 nm for near infrared) were collected from five subplots within each of the 42 plots. The decision tree algorithm of data mining technology was used to distinguish between manure and chemical fertilizer treatments. The success of the classification rate was as high as 91% for the early planting season, 99% for the mid planting season, and 95% for the late planting season. The accuracy of the results, demonstrates that data mining technology could be used for remote sensing imagery classification of fertilizer applications.

(Download PDF)    (Export to EndNotes)