Monitoring crop growth is of great significance to obtain crop growth status information for development of smart agriculture. The traditional way to measure the phenotypic parameters of crops is labor-intensive and encounters inconvenient operations. In this study, we propose to obtain the phenotypic parameters of crops from 3-D reconstruction of plants from single RGB images using a data-driven plant phenotypic parameters estimation network (P3ES-Net) deep neural network, which enables to estimate the depth shift and camera focal length used for depth estimation and reconstruction of the 3-D model of plants. Based on the principles of the monocular ranging and pinhole imaging model, crop phenotypic parameters such as height, canopy size, and trunk diameter can then be calculated from the 3-D model. Experiments with four practical plants present that our method is able to achieve acceptable evaluation of the growth status of plants. Of more significance, it achieves particular superior depth estimation performance over a commercial depth camera, which is a very new on-sale depth camera using stereo vision and deep learning network. This potential performance throws light on the low-cost measurement of crop phenotypic parameters using RGB camera in monitoring crop growth.

中文翻译：

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使用基于深度学习的 3-D 重建从单个 RGB 图像中估计植物的表型参数

监测作物生长情况对于获取作物生长状态信息对于发展智慧农业具有重要意义。传统的农作物表型参数测量方法劳动强度大，操作不便。在这项研究中，我们建议使用数据驱动的植物表型参数估计网络 (P3ES-Net) 深度神经网络从单个 RGB 图像的植物 3-D 重建中获得作物的表型参数，该网络能够估计深度偏移以及用于深度估计和重建植物 3-D 模型的相机焦距。基于单目测距和针孔成像模型的原理，然后可以从 3-D 模型计算作物表型参数，例如高度、冠层大小和树干直径。对四种实际植物的实验表明，我们的方法能够对植物的生长状态进行可接受的评估。更重要的是，它实现了比商用深度相机特别优越的深度估计性能，商用深度相机是一款非常新的使用立体视觉和深度学习网络的深度相机。这种潜在的性能揭示了使用 RGB 相机监测作物生长的低成本测量作物表型参数。