Probabilistic multi SVM weed species classification for weed scouting and selective spot weeding

In this paper, a probabilistic output multi SVMs were used to classify the weed seedlings into groups for spot spraying and weed scouting application. Weeds in the samples are collected at approximely 1–4 weeks after post emergence. The weed seedlings are classified using Support Vector machines while feature selection and fine tuning of classifier parameters were fine tuned using genetic algorithm. The features which included regional shapes parameters, fractal dimensions and elliptical Fourier coefficients, skeleton statistics, boundary to centroid and colour statistics were extracted from individual leaves and the overall binarized shape of the weed seedlings. The resulting SVM ensemble classifier is able to classify the various weed seedlings into various classes at a reasonable rate which can be further improved by enlarging training sets and improving individual SVMs.