Good Fruit Grower

November 2012

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crop estimating Automated An accurate count of the apples across R the whole orchard would help growers and packers be well prepared for harvest. by Geraldine Warner obotics experts at Carnegie Mellon University in Pennsylvania are confident they can develop a machine able to count apples in orchards in order to provide growers with accurate crop estimates and yield maps. And it may be feasible for the machine to estimate the size of the apples. Dr. Stephen Nuske, project scientist at Carnegie Mel- lon's Field Robotics Center, said a precise estimate of the crop could help growers and packers better plan for har- vest and know exactly how many bins they will need in the various parts of the orchard and how much transport and storage will be required. "Knowing those yields can really optimize the effi- ciency of their operations," he said. "The other big thing we think it can be useful for is reducing the variability of the orchards. By knowing where the fruit is, you can take actions in the orchard to increase your yields in underperforming areas." It might be possible, for example, to change nutrient, irrigation, or pruning strategies to increase production, he suggested. The California company Vision Robotics began devel- oping an automated apple crop estimation system six years ago with funding from the Washington Tree Fruit Research Commission. It was conceived as the first step in developing a robotic apple harvesting system. The company later was a cooperator in the national Compre- hensive Automation for Specialty Crops project that was supported with federal funding. Now, Nuske and his col- leagues, Dr. Sanjiv Singh and Dr. Marcel Bergerman, who are involved in the CASC project, have proposed an alter- native crop estimation system. They have already worked on robotic crop estimation in strawberries, grapes, and other crops, and are confident that a commercial system can be developed. Cameras "We've done a lot of groundwork in having a hardware and software system capable of taking images of the trees and vines and detecting the fruit and counting it," Nuske said. CMU's crop estimation system's hardware consists of two high-resolution cameras with wide-angle lenses EPA Certified V-10 Engine [without catalytic converter] Diagram of the apple yield estimator under development at Carnegie Mellon University. Go CAT Less Simplify Your Life [without Catalytic Converter] 5 Lower initial purchase price 5 Lower maintenance cost 5 Easier to diagnose and service 5 No more CAT burn injuries 5 Increased return on investment 5 Reduced theft losses, eliminate costly CAT replacements 5 Eliminates need for oxygen sensors FORD TRITON V-10 and two ring flashes on an aluminum mount. Nuske and Singh mounted the system on an autonomous orchard platform that they developed, which can travel down the rows at a consistently slow speed (about half a mile per hour). However, the system could be mounted on a manually-driven vehicle. As the system goes down the row, it photographs the Chamberlin Distributing Co., Inc. Wenatchee, WA 509-663-7151 our newest dealer Introducing trees, taking one image per second, which means it cap- tures multiple images of each fruit as it goes by. A high- precision GPS device is used to determine the exact location of each apple and ensure an apple seen from both sides of the tree is not counted twice. Nuske said it would be easy to calculate the size of the fruit. "All you need to know is the distance of the apple from the vehicle and the size of the apple in the image." The speed it travels down the row could be increased if it proves possible to have an accurate count with fewer images or if the cameras could capture images faster. There's no reason, Nuske said, why it couldn't ultimately operate ten times as fast. Call for a dealer in your area! COMPANY INC. H.F . HAUFF Toll Free 1-855-855-0318 509-248-0318 • fax 509-248-0914 2921 Sutherland Park Dr. Yakima, WA 98903-1891 12 NOVEMBER 2012 GOOD FRUIT GROWER Night When the scientists began their crop estimation work on strawberries, they operated the system during the day, but found that it performed more consistently with con- trolled lighting at night. The images are in color. The soft- ware uses hue, saturation, and value to distinguish red apples from other objects in the orchard, such as wires, trunks, branches, and foliage. A different method, which uses color intensity, is used to detect green apples. Apples have a stronger green color than foliage. Light reflected from the objects in the image is used to distinguish the shape and location of the apples. An experimental crop yield estimator travels down an orchard row at a speed of about half a mile per hour. Researchers believe that ultimately it could do the job ten times as fast.

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