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Shape-based Segmentation of Tomatoes for Agriculture Monitoring

In this paper, we present a segmentation procedure based on a parametric active contour with shape constraint, in order to follow the growth of the tomatoes from the images acquired in the field. This is a challenging task because of the poor contrast in the images and the occlusions by the vegetation. In our sequential approach, considering one image per day, we assume that a segmentation of the tomatoes is available for the image acquired the previous day. An initial curve for the active contour model is computed by combining gradient information and region information. Then, an active contour with shape constraint is applied to provide an elliptic approximation of the tomato boundary. We performed a quantitative evaluation of our approach. Given the varying degree of occlusion in the images, the image data set was divided into three categories, based on the occlusion degree of the tomato in the processed image. For the cases with low occlusion, good results were obtained, with an average relative distance between the manual segmentation and the automatic segmentation of 2.73% (expressed as percentage of the size of tomato). For the images with significant amount of occlusion, a good segmentation was obtained on most of them (44%), where the average error was less than 10%.