Researchers at Carnegie Mellon University's National Robotics Engineering Center (NREC) have developed a machine that uses computer vision and machine learning to inspect, grade, and sort strawberries. The researchers say the machine classifies and sorts harvested plants faster and more consistently than human workers can. The strawberry plant sorter uses computer vision to examine strawberries as they pass by on a conveyor belt. The sorter is taught how to classify strawberry plants by size, variety, and stage of growth using machine-learning algorithms.
The researchers say the sorter could make strawberry nurseries much more efficient, and improve quality, streamline production, and deliver better strawberry plants to growers. NREC engineers tested the sorter under realistic conditions, accounting for variables such as weather conditions that can change the plant's appearance. On average, the machine sorted 5,000 plants per hour, several times faster than human sorting. NREC researchers believe the maximum sorting rate could reach 20,000 to 30,000 plants per hour.
The successful field test concluded stage two of a five-stage program that will develop a machine ready for commercial use.
From Carnegie Mellon News
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