Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning.
Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning.
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DOI：
10.3390/s21248425
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发表时间：
2021-12-17
期刊：
影响因子：
--
通讯作者：
Rousseau D
中科院分区：
文献类型：
--
作者：
Garbouge H;Rasti P;Rousseau D
利用高通量表型与成像和机器学习来监测幼苗生长是植物研究中一个艰难而有趣的课题。最近，低成本的RGB成像传感器和白天的深度学习解决了这个问题。rgb深度成像设备也可以以低成本获得，这为延长白天和黑夜对幼苗的监测提供了机会。本文探讨了将RGB成像与深度成像融合在幼苗生长阶段监测中的附加价值。我们提出了一种基于RGB-Depth融合的深度学习架构来对幼苗生长的前三个阶段进行分类。结果表明，与白天单独使用RGB图像相比，正确识别率有平均的性能提高。在早期将RGB和Depth进行融合，可以获得最好的性能。此外，深度显示能够在没有光的情况下检测生长阶段。
The use of high-throughput phenotyping with imaging and machine learning to monitor seedling growth is a tough yet intriguing subject in plant research. This has been recently addressed with low-cost RGB imaging sensors and deep learning during day time. RGB-Depth imaging devices are also accessible at low-cost and this opens opportunities to extend the monitoring of seedling during days and nights. In this article, we investigate the added value to fuse RGB imaging with depth imaging for this task of seedling growth stage monitoring. We propose a deep learning architecture along with RGB-Depth fusion to categorize the three first stages of seedling growth. Results show an average performance improvement of correct recognition rate by comparison with the sole use of RGB images during the day. The best performances are obtained with the early fusion of RGB and Depth. Also, Depth is shown to enable the detection of growth stage in the absence of the light.
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