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Suraj Amatya Washington State University/Biological Systems Engineering : + 1 5097885043 Email : [email protected] Goal Detecting cherry tree branches and locating shaking positions for automatic harvesting Conclusion • Automated cherry harvesting is possible using machine vision for branch detection • Morphology of branch sections and cherries can be used to detect branches in full foliage canopies • Overall 91% of branches detected • 93.5% cherries harvested by shaking at points picked by the algorithm Branch Detection • Estimated branch segment features o Orientation, Major Axis Length, Minor Axis Length • Grouped segments of the same branch • Merged branch sections using region growing algorithm • Fitted linear/logarithmic equations to branches Cherry Detection • Identified and segmented cherry clusters • Grouped cherry clusters located along a vertical direction • Determined orientation of branch • Fitted branch equations Shaking Point Localization • Located cherries in detected branches • Divided tree canopy into 3 shaking zones • Shaking position identified close to larger cherry clusters • Obtained distance of shaking points from 3D camera Original Image Segmented Branches Grouped Branch Sections Detected Branches Original Image Vertical Cherry Cluster Search Detected Branches Localization of shaking points for harvesting Results Harvested Not harvested (within FOV) Not Harvested (beyond FOV) FOV = Field OF View Weight (lb.) 306.7 12.0 9.4 Percent (%) 93.5 3.7 2.9 Table 2 : Results of cherry harvesting trials Table 1: Results of branch detection method based on branch pixels and cherry pixels Actual Detected False Detection True Detection Undetected Vertical No. of Branches 453 477 73 404 49 Percentage (%) 100.0% 105.3% 16.1% 89.2% 10.8% Y -trellis No. of Branches 453 481 56 425 28 Percentage (%) 100.0% 106.2% 12.4% 93.8% 6.2%
