On-tree fruit recognition using texture properties and color data

As a prelude to using stereo vision to accurately locate apples in an orchard, this paper presents a vision based algorithm to locate apples in a single image. On-tree situations of contrasting red and green apples as well as green apples in the orchard with poor contrast have been considered. The study found out that the redness in both cases of red and green apples can be used to differentiate apples from the rest of the orchard. Texture based edge detection has been combined with redness measures, and area thresholding followed by circle fitting, to determine the location of apples in the image plane. In the case of severely cluttered environments, Laplacian filters have been used to further clutter the foliage arrays by edge enhancement so that texture differences between the foliage and the apples increased thereby facilitating the separation of apples from the foliage. Results are presented that show the recognition of red and green apples in a number of situations as well as apples that are clustered together and/or occluded.

I. Introduction

The methodologies and results presented in this paper are directed towards robotic fruit picking. Specifically, the problem of recognizing apples in an orchard as a fruit picking machine gradually traverse the orchard is addressed. The ultimate aim is to use a pair of stereo cameras as part of the set up shown in Fig. 1 so that the manipulator can pick the apples. The manipulator is mounted on a pair of telescopic linear stages so that the robot can reach areas that are outside the width of the tractor. By lifting and lowering the loader, the height of the robot can be adjusted. As a result the cameras looking perpendicular to the direction of travel of the tractor will be able to image the trees with apples.