Kernel Fisher Discriminant Method Research Articles

Classification of soil and vegetation by kernel fisher and kernel PCA

Precision Agriculture is concerned with all sort of within-field variability, spatially and temporally, that reduces the efficacy of agronomic practices applied in a uniform way all over the field. Because of these sources of heterogeneity, uniform management actions strongly reduce the efficiency of the resource input to the crop (i.e., fertilization, water) or for the agrochemicals used for pest control (i.e. herbicide). In particular, weed plants are one of these sources of variability for the crop, as they occur in patches in the field. Detecting the location, size and internal density of these patches, along with identification of main weed species involved, open the way to a site-specific weed control strategy, where only patches of weeds would receive the appropriate herbicide (type and dose). Herein, the first stage of recognition method of vegetal species, the classification of soil and vegetation, is described and is based upon the kernel Fisher discriminant method (KFDM) and on Kernel Principal Analysis (KPCA).

A reformative kernel Fisher discriminant analysis

A reformative kernel Fisher discriminant method is proposed, which is directly derived from the naive kernel Fisher discriminant analysis with superiority in classification efficiency. In the novel method only a part of training patterns, called “significant nodes”, are necessary to be adopted in classifying one test pattern. A recursive algorithm for selecting “significant nodes”, which is the key of the novel method, is presented in detail. The experiment on benchmarks shows that the novel method is effective and much efficient in classifying.