A Vision-based Classifier in Precision Agriculture Combining Bayes and Support Vector Machines

One important 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. In order to reach this goal, two major factors need to be considered: 1) the similar spectral signature, shape and texture between weeds and crops; 2) the irregular distribution of the weeds within the crop's field. This paper outlines an automatic computer vision system for the detection and differential spraying of Avena sterilis, a noxious weed growing in cereal crops. The proposed system involves two processes: image segmentation and decision making. Image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based attributes measuring the relations among the crops and the weeds. From these attributes, a hybrid decision making approach, under a Bayesian framework determines, if a cell must be or not sprayed. The hybrid approach uses the support vector machines for computing the prior probability in this Bayesian framework. This makes the main finding of this paper. The method performance is compared against other available strategies.