Segmentation of Tomatoes in Open Field Images with Shape and Temporal Constraints

With the aim of estimating the growth of tomatoes during the agricultural season, we propose to segment tomatoes in images acquired in open field, and to derive their size from the segmentation results obtained in pairs of images acquired each day. To cope with difficult conditions such as occlusion, poor contrast and movement of tomatoes and leaves, we propose to base the segmentation of an image on the result obtained on the image of the previous day, guaranteeing temporal consistency, and to incorporate a shape constraint in the segmentation procedure, assuming that the image of a tomato is approximately an ellipse, guaranteeing spatial consistency. This is achieved with a parametric deformable model with shape constraint. Results obtained over three agricultural seasons are very good for images with limited occlusion, with an average relative distance between the automatic and manual segmentations of 6.46% (expressed as percentage of the size of tomato).