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.

Authors

• Jun Zhao
• Joel Tow
• Jay Katupitiya

Keywords

• Sensor arrays
• Image edge detection
• Image recognition
• Robot vision systems
• Cameras
• Manipulators
• Sorting
• Neural networks
• Chemical sensors
• Mechanical factors
• Textural Properties
• Fruit Recognition
• South Wales State Government
• Edge Detection
• Area Threshold
• Red Apple
• Edge Enhancement
• Low Values
• Post Processing
• Red Values
• Contour Shape
• Contour Points
• Background Texture
• Low Texture
• Texture properties
• redness
• image processing