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首页> 外文会议>IEEE International Conference on Computer Vision Workshops >Deep Learning for Multi-task Plant Phenotyping
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Deep Learning for Multi-task Plant Phenotyping

机译：用于多任务植物表型的深度学习

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Plant phenotyping has continued to pose a challenge to computer vision for many years. There is a particular demand to accurately quantify images of crops, and the natural variability and structure of these plants presents unique difficulties. Recently, machine learning approaches have shown impressive results in many areas of computer vision, but these rely on large datasets that are at present not available for crops. We present a new dataset, called ACID, that provides hundreds of accurately annotated images of wheat spikes and spikelets, along with image level class annotation. We then present a deep learning approach capable of accurately localising wheat spikes and spikelets, despite the varied nature of this dataset. As well as locating features, our network offers near perfect counting accuracy for spikes (95.91%) and spikelets (99.66%). We also extend the network to perform simultaneous classification of images, demonstrating the power of multi-task deep architectures for plant phenotyping. We hope that our dataset will be useful to researchers in continued improvement of plant and crop phenotyping. With this in mind, alongside the dataset we will make all code and trained models available online.
机译：多年来，植物表型一直对计算机视觉构成挑战。特别需要精确量化农作物的图像，而这些植物的自然变异性和结构也带来了独特的困难。最近，机器学习方法在计算机视觉的许多领域都显示出令人印象深刻的结果，但是这些方法依赖于目前尚无法用于农作物的大型数据集。我们提供了一个称为ACID的新数据集，该数据集提供了数百个小麦小穗和小穗的准确注释图像，以及图像级别的注释。然后，我们提供了一种深度学习方法，尽管该数据集的性质各不相同，但该方法能够准确地定位小麦穗和小穗。除了定位功能，我们的网络还为尖峰（95.91 \％）和小尖峰（99.66 \％）提供近乎完美的计数精度。我们还扩展了网络以执行图像的同时分类，从而证明了用于植物表型的多任务深度架构的强大功能。我们希望我们的数据集将对研究人员在持续改善植物和农作物表型方面有所帮助。考虑到这一点，除了数据集外，我们还将在线提供所有代码和训练有素的模型。

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