Good Fruit Grower

November 2012

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New Technology percent lower than the human count, but, after calibration, it was within 1.2 percent of the human count. Further tests were conducted with The yellow circles represent the locations of apples, as calculated by the software. Images are taken at night, under controlled lighting. Honeycrisp this September at Pennsylva- nia State University's Fruit Research and Extension Center in Biglerville. Data are still being analyzed. The four-year CASC project will end this year, but Nuske will continue to work on generic crop estimation systems for another three years with USDA funding. The National Wine and Grape Initiative is supporting the grape research and is interested in seeing a crop estimator commercialized. Nuske suggests that not every grower would purchase a yield estimation sys- tem. Rather, a service company might buy one and do yield estimation for growers on a contract or service basis. • The raw image taken by the crop yield estimator's cameras. Results from orchard trials show that the robotic yield estimator is most accu- rate in high-density, trellised plantings with two-dimensional trees, where most of the fruit are visible. It is far less accurate in traditional plantings with widely spaced large trees where much of the fruit is hidden from view, or "occluded," to use the scientific term. "We can count all the visible apples," Nuske said. "But there's no method where you can count an apple that's not visible." However, if the number of hidden apples is consistent, which was the case in the trials, the estimation system could be calibrated by having humans count all the apples on sample trees then calculating the percentage of apples the machine is missing. This would not necessarily have to be done each year or for each orchard, Nuske said. Similar orchards might be able to use the same calibration factor. In their grape studies, they worked in two similar-styled vineyards—one in New York, and the other in California—and were able to use the same calibration in California as in New York without needing to take manual samples again. The scientists tested the apple yield estimation system in 2011 with Red Deli- cious and Granny Smith apples at a Wash- ington State University research orchard near Wenatchee. The estimate for red apples on a high-density system was quite accurate at 3.2 percent lower than an esti- mate made by humans counting each apple. They also tested it in a block of green apples, which had not been fruit thinned, had large clusters of apples and more foliage. As a result, many apples could not be detected because they were hidden by other apples or leaves. In that block, the machine estimate was 30 GOOD FRUIT GROWER NOVEMBER 2012 13 PhotoS CoURtESY CARNEGIE MELLoN UNIVERSItY

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