DIFFERENTIATION OF CROP AND WEEDS BY DECISION-TREE ANALYSIS OF MULTI-SPECTRAL DATA

The purpose of this study was to use a data mining technique (i.e., decision trees) to classify multi-spectral images of experimental plots having different crop and weed populations. Eleven types of plots were prepared for this study. Eight types were seeded with corn or soybeans and were either: (1) weed-free, (2) co-populated by velvetleaf only, (3) co-populated with a mixture of grass species, or (4) co-populated with the predominant weed species of the regions. The other three types were as (2), (3), and (4) with neither corn nor soybeans. An aircraft-mounted pushbroom imaging spectrometer was used to obtain scans of the plots in one blue, five green, five red, and thirteen infrared bands. Eight classification problems involving different degrees of recognition complexity were set up. Each was tested using three different input types from the multi-spectral data. The three types of input were: (a) absolute values of radiance from the 24 wavebands; (b) vegetation index (VI), which consists of 12 inputs; and (c) normalized difference vegetation index (NDVI), which consists of 65 inputs. Results showed that the most complex classification problem (distinguishing between 11 crop/weed combinations) was best resolved using the NDVI inputs (success classification of 0.85 as compared with 0.79 and 0.55 for the absolute radiance and VI, respectively). Moreover, NDVI performed best as inputs in seven out of the eight problems.