Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.
Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.
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DOI：
10.1093/gigascience/gix083
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发表时间：
2017-10-01
期刊：
影响因子：
9.2
通讯作者：
French AP
中科院分区：
文献类型：
--
作者：
Pound MP;Atkinson JA;Townsend AJ;Wilson MH;Griffiths M;Jackson AS;Bulat A;Tzimiropoulos G;Wells DM;Murchie EH;Pridmore TP;French AP
在植物表型分析中，能够测量大图像集上的许多特征以帮助遗传发现已经变得重要。现在通常由机器人捕获的数据集的大小通常排除了手动检查，因此有动机寻找全自动方法。深度学习是一个新兴的领域，在许多数据分析问题上有望取得无与伦比的结果。基于人工神经网络，深度方法在网络中有更多的隐藏层，因此具有更强的辨别力和预测能力。我们展示了使用这种方法作为植物表型管道的一部分。我们展示了这些技术在应用于基于图像的植物表型分析这一具有挑战性的问题时所取得的成功，并展示了根和芽特征识别和定位的最新结果（>97%的准确率）。我们使用深度学习的全自动性状识别来识别根构型数据集中的数量性状基因座。大多数（14个中的12个）手动识别的数量性状基因座也是使用我们基于深度学习检测的自动化方法发现的，以定位植物特征。我们已经证明，基于深度学习的表型分析在验证和测试图像集时具有非常好的检测和定位精度。我们已经证明，这些特征可以用于获得有意义的生物性状，这反过来又可以用于数量性状基因座发现管道。这个过程可以完全自动化。我们预测，在足够的训练集下，这种深度学习方法会带来基于图像的表型分析的范式转变。
In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning–based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets.
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影响因子：
64.8
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