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Citrus Yield Mapping System on a Canopy Shake and Catch Harvester

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  2007 ASAE Annual Meeting  073050.(doi:10.13031/2013.23175)
Authors:   Radnaabazar Chinchuluun, Won Suk Lee, Reza Ehsani
Keywords:   Canopy shake and catch harvester, Citrus, Machine vision, Yield mapping

Machine vision based citrus yield mapping and fruit quality inspection system was developed. The system consisted of 3CCD camera, four halogen lamps, a DGPS receiver, an encoder and a laptop computer. A total of 3,653 images were taken during an experiment on the test bench at the Citrus Research and Education Center, Lake Alfred, Florida and 703 images were used for analysis. The system was also tested on a canopy shake and catch harvester at the Lykes grove located in Fort Basinger, FL. A total of 773 images were acquired as well. Fruit weight was measured in 14 test trials of image acquisitions during the test bench experiment as well as in two test trials of image acquisitions during a field trial with a commercial canopy shake and catch harvester. An image processing algorithm that could identify and inspect fruit qualities was developed. The number of fruit and total fruit areas were measured from the sets of color images using the developed algorithm. Finally number of citrus fruit that was found by the image processing algorithm during the test-bench experiment was compared against actual fruit weight. The coefficient of determination, R2, was 0.962 between them. For a validation purpose of the canopy shake and catch harvester experiment, number of fruit was counted manually from a total of 60 images. Human counting was compared with algorithm counting. The coefficient of determination, R2, was 0.891 between actual and estimated number of fruit.

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