Automated machine learning (AutoML) has been heralded as the next wave in artificial intelligence with its promise to deliver high-performance end-to-end machine learning pipelines with minimal effort from the user. However, despite AutoML showing great promise for computer vision tasks, to the best of our knowledge, no study has used AutoML for image-based plant phenotyping. To address this gap in knowledge, we examined the application of AutoML for image-based plant phenotyping using wheat lodging assessment with unmanned aerial vehicle (UAV) imagery as an example. The performance of an open-source AutoML framework, AutoKeras, in image classification and regression tasks was compared to transfer learning using modern convolutional neural network (CNN) architectures. For image classification, which classified plot images as lodged or non-lodged, transfer learning with Xception and DenseNet-201 achieved the best classification accuracy of 93.2%, whereas AutoKeras had a 92.4% accuracy. For image regression, which predicted lodging scores from plot images, transfer learning with DenseNet-201 had the best performance (R² = 0.8303, root mean-squared error (RMSE) = 9.55, mean absolute error (MAE) = 7.03, mean absolute percentage error (MAPE) = 12.54%), followed closely by AutoKeras (R² = 0.8273, RMSE = 10.65, MAE = 8.24, MAPE = 13.87%). In both tasks, AutoKeras models had up to 40-fold faster inference times compared to the pretrained CNNs. AutoML has significant potential to enhance plant phenotyping capabilities applicable in crop breeding and precision agriculture.

中文翻译：

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基于高通量图像的植物表型自动机器学习

自动化机器学习（AutoML）被誉为人工智能的下一波浪潮，它有望以最少的用户努力提供高性能的端到端机器学习管道。然而，尽管就我们所知，尽管AutoML对计算机视觉任务显示出了巨大希望，但没有研究将AutoML用于基于图像的植物表型分析。为了解决这一知识差距，我们以小麦倒伏评估和无人飞行器（UAV）图像为例，研究了AutoML在基于图像的植物表型分析中的应用。将开放源代码AutoML框架AutoKeras在图像分类和回归任务中的性能与使用现代卷积神经网络（CNN）架构的转移学习进行了比较。对于图像分类，将地块图像分类为已放置或未放置，Xception和DenseNet-201进行的转移学习获得了93.2％的最佳分类准确度，而AutoKeras的准确度为92.4％。对于通过回归图预测倒塌得分的图像回归，使用DenseNet-201进行的转移学习具有最佳性能（R² = 0.8303，均方根误差（RMSE）= 9.55，平均绝对误差（MAE）= 7.03，平均绝对百分比误差（MAPE）= 12.54％），紧随其后的是AutoKeras（R ² = 0.8273，RMSE = 10.65， MAE = 8.24，MAPE = 13.87％）。在这两项任务中，AutoKeras模型的推理时间均比预训练的CNN快40倍。AutoML具有极大的潜力，可以增强适用于作物育种和精确农业的植物表型能力。