Application of support vector machine technology for weed and nitrogen stress detection in corn

This study was conducted to evaluate the usefulness of a new method in artificial intelligence, the support vector machine (SVM), as a tool for classifying hyperspectral images taken over a corn (Zea mays L.) field. The classification was performed with respect to nitrogen application rates and weed management practices, and the classification accuracy was compared with those obtained by an artificial neural network (ANN) model on the same data. The field experiment consisted of three nitrogen application rates and four weed management strategies. A hyperspectral image was obtained with a 72-waveband Compact Airborne Spectrographic Imager, at an early growth stage during the year 2000 growing season. Nitrogen application rates were 60, 120, and 250 kg N/ha. Weed controls were: none, control of grasses, control of broadleaf weeds, and full weed control. Classification accuracy was evaluated for three cases: combinations of nitrogen application rates and weed infestation levels, nitrogen application rates alone, and weed controls alone. The SVM method resulted in very low misclassification rates, as compared to the ANN approach for all the three cases. Detection of stresses in early crop growth stage using the SVM method could aid in effective early application of site-specific remedies to timely in-season interventions.