CLASSIFICATION ACCURACY OF DISCRIMINANT ANALYSIS, ARTIFICIAL NEURAL NETWORKS, AND DECISION TREES FOR WEED AND NITROGEN STRESS DETECTION IN CORN

By Y. Karimi, Shiv O. Prasher, H. Mcnairn, R. Bonnell, P. Dutilleul, P. Goel. 2005

52 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Ecology

View Paper: https://www.semanticscholar.org/paper/362d1ac8fa993a8c8e9bd91e835a2cbd54e17907

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Amparo Alonso-Betanzos

In Ecology

1. Francisca López-Granados
2. José Manuel Peñá-Barragán

Citations in the corpus

• Multispectral classification of grass weeds and wheat (Triticum durum) using linear and nonparametric functional discriminant analysis and neural networks (2008, methodology): “Karimi et al. (2005) tested three classification models for distinguishing weeds at different growth stages and nitrogen treatments, and found that a classic discriminant analysis performed better than ANNs for the earlier phenological stages.”