A Vision-Based Hybrid Classifier for Weeds Detection in Precision Agriculture Through the Bayesian and Fuzzy k-Means Paradigms

One objective in Precision Agriculture is to minimize 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 hybrid decision making system based on the Bayesian and Fuzzy k-Means (FkM) classifiers, where the a priori probability required by the Bayes framework is supplied by the FkM. This makes the main finding of this paper. The method performance is compared against other available strategies.

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