Working Paper Article Version 1 This version is not peer-reviewed

Automated Machine Learning for High-Throughput Image-Based Plant Phenotyping

Version 1 : Received: 29 January 2021 / Approved: 1 February 2021 / Online: 1 February 2021 (14:11:08 CET)

A peer-reviewed article of this Preprint also exists.

Koh, J.C.; Spangenberg, G.; Kant, S. Automated Machine Learning for High-Throughput Image-Based Plant Phenotyping. Remote Sens. 2021, 13, 858. Koh, J.C.; Spangenberg, G.; Kant, S. Automated Machine Learning for High-Throughput Image-Based Plant Phenotyping. Remote Sens. 2021, 13, 858.

Journal reference: Remote Sens. 2021, 13, 858
DOI: 10.3390/rs13050858

Abstract

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 UAV imagery as an example. We compared the performance of an open-source AutoML framework, AutoKeras in image classification and regression tasks 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 best classification accuracy of 93.2%, whereas Autokeras had 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, RMSE=9.55, MAE=7.03, MAPE=12.54%), followed closely by AutoKeras (R2=0.8273, RMSE=10.65, MAE=8.24, MAPE=13.87%). Interestingly, in both tasks, AutoKeras models had up to 40-fold faster inference times compared to the pretrained CNNs. The merits and drawbacks of AutoML compared to transfer learning for image-based plant phenotyping are discussed.

Keywords

automated machine learning; Neural Architecture Search; high-throughput plant phenotyping; wheat lodging assessment; unmanned aerial vehicle.

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