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首页> 外文会议>American Society of Agricultural and Biological Engineers Annual International Meeting >Development of A Mushroom Harvesting Assistance Systemusing Computer Vision
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Development of A Mushroom Harvesting Assistance Systemusing Computer Vision

机译：使用计算机愿景开发蘑菇采伐系统

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Conventional mushroom harvesting relies on manual labors, which is one of the major reasons for increased production costs. Different maturation speed among individual mushrooms promotes farm workers' efforts in selective harvesting. To assist in robotic harvesting of mushrooms, a computer vision system was developed to detect an individual mushroom from mushroom clusters and evaluate maturity of the mushroom. Specific objectives of this study were to (I) solve the overlapping problem and differentiate each mushroom from a mushroom cluster, and (2) develop a machine vision algorithm to identify maturity using the size and shape of mushrooms. For mushroom identification, faster R-CNN model was developed to distinguish mushrooms from substrate. A 3D pointcloud of mushroom was acquired by a depth camera and used to segment an individual crop among the overlapped mushrooms in clusters. After the segmentation, the size of mushroom caps was calculated using the pointcloud and the shape of mushroom caps was quantified using normal vectors. The accuracy of maturity recognition reached 70.93 %. The results of this study can be extended to a commercial scale and enhance mushroom harvesting efficiency by reducing the overall cost of mushroom production.
机译：传统的蘑菇收获依赖于手动劳动力，这是提高生产成本的主要原因之一。各个蘑菇之间的不同成熟速度促进了农业工人在选择性收获方面的努力。为了协助蘑菇的机器人收获，开发了一种计算机视觉系统，以检测来自蘑菇群的单独蘑菇，评估蘑菇的成熟度。本研究的具体目标是（i）解决重叠的问题并将每个蘑菇与蘑菇簇区分开，并且（2）开发机器视觉算法，以使用蘑菇的尺寸和形状来识别成熟度。对于蘑菇鉴定，开发了更快的R-CNN模型以区分蘑菇从基材。通过深度相机获得蘑菇的3D点阵线，并用于在集群中分段重叠的蘑菇中的单个作物。分段后，使用POINLCLOUD计算蘑菇帽的尺寸，使用常规载体量化蘑菇帽的形状。到期识别的准确性达到70.93％。该研究的结果可以通过降低蘑菇生产的总成本来扩展到商业规模并增强蘑菇采伐效率。

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