Inderscience Publishers

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

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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.

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