Segmentation and size estimation of tomatoes from sequences of paired images

International audienceIn this paper, we present a complete system to monitor the growth of tomatoes from images acquired in open fields. This is a challenging task because of the severe occlusion and poor contrast in the images. We approximate the tomatoes by spheres in the 3D space, hence by ellipses in the image space. The tomatoes are first identified in the images using a segmentation procedure. Then, the size of the tomatoes is measured from the obtained segmentation and camera parameters. The shape information combined with temporal information, given the limited evolution from an image to the next one, is used throughout the system to increase the robustness with respect to occlusion and poor contrast. The segmentation procedure presented in this paper is an extension of our previous work based on active contours. Here, we present a method to update the position of the tomato by comparing the SIFT descriptors computed at predetermined points in two consecutive images. This leads to a very accurate estimation of the tomato position, from which the entire segmentation procedure benefits. The average error between the automatic and manual segmentations is around 4 % (expressed as the percentage of tomato size) with a good robustness with respect to occlusion (up to 50 %). The size estimation procedure was evaluated by calculating the size of tomatoes under a controlled environment. In this case, the mean percentage error between the actual radius and the estimated size is around 2.35 % with a standard deviation of 1.83 % and is less than 5 % in most (91 %) cases. The complete system was also applied to estimate the size of tomatoes cultivated in open fields

Segmentation of tomatoes in open field images with shape and temporal constraints

International audience<p>With the aim of estimating the growth of tomatoes duringthe agricultural season, we propose to segment tomatoes in imagesacquired in open field, and to derive their size from the segmentationresults obtained in pairs of images acquired each day. To cope withdifficult conditions such as occlusion, poor contrast and movement oftomatoes and leaves, we propose to base the segmentation of an imageon the result obtained on the image of the previous day, guaranteeingtemporal consistency, and to incorporate a shape constraint in the segmentationprocedure, assuming that the image of a tomato is approximatelyan ellipse, guaranteeing spatial consistency. This is achieved witha parametric deformable model with shape constraint. Results obtainedover three agricultural seasons are very good for images with limitedocclusion, with an average relative distance between the automatic andmanual segmentations of 6.46% (expressed as percentage of the size oftomato).</p