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Melon yield prediction using small unmanned aerial vehicles

机译:使用小型无人机的甜瓜产量预测

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摘要

Thanks to the development of camera technologies, small unmanned aerial systems (sUAS), it is possible to collect aerial images of field with more flexible visit, higher resolution and much lower cost. Furthermore, the performance of objection detection based on deeply trained convolutional neural networks (CNNs) has been improved significantly. In this study, we applied these technologies in the melon production, where high-resolution aerial images were used to count melons in the field and predict the yield. CNN-based object detection framework-Faster R-CNN is applied in the melon classification. Our results showed that sUAS plus CNNs were able to detect melons accurately in the late harvest season.
机译:由于摄像机技术的发展,小型无人机系统(sUAS),可以以更灵活的访问,更高的分辨率和更低的成本收集野外航拍图像。此外,基于深度训练的卷积神经网络(CNN)的异物检测性能已得到显着提高。在这项研究中,我们将这些技术应用于甜瓜生产中,在该生产中,高分辨率的航空影像被用于对实地的甜瓜进行计数并预测产量。基于CNN的目标检测框架Faster R-CNN被应用于瓜类分类。我们的结果表明,sUAS加上CNN能够在收获后期准确地检测瓜。

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