“On combining vision-based hybrid classifiers for weeds detection in precision agriculture”

One objective in precision agriculture is to minimise the volume of herbicides that are applied to the fields through the use of site-specific weed management systems. 

This paper outlines an automatic computer vision system for the detection and differential spraying of Avena sterilis, a noxious weed growing in cereal crops. With such purpose we have designed a multiple hybrid decision making system based on four different simple classifiers: Bayes, fuzzy k-means

(FkM), support vector machines (SVM) and Hebbian learning. The performance of this multiple classifier is compared against other approaches, including simple versions of hybrid classifiers.