PA—Precision Agriculture: Use of Hyperspectral Imagery for Identification of Different Fertilisation Methods with Decision-tree Technology

Abstract

This paper introduces data mining technology designed to classify agricultural fields under different manure/fertiliser application strategies. During the summer of 2000, airborne hyperspectral data were collected three times at two field sites in southwestern Quebec, Canada. One field site contained 24 plots (20 m by 24 m) that were amended with manure treatments and planted with maize and soya beans. The second field site contained 18 plots (18·5 m by 75 m) that received chemical fertilisers and were planted with maize. Reflectances of 71 wave bands of hyperspectral data (400 nm for violet to 940 nm for near infrared) were collected from 5 subplots within each of the 42 plots. The decision-tree algorithm of data mining technology was used to distinguish between manure and chemical fertiliser treatments. The decision-tree algorithm divides the data to reduce the deviance, and classifies them into the pre-defined categories as many tree branches. 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 fertiliser applications.